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Predictors of acute stress disorder severity
Journal of Affective Disorders 149 (2013) 277–281
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research report
Predictors of acute stress disorder severity Sharain Suliman a,n, Zyrhea Troeman b, Dan J. Stein a,c, Soraya Seedat a,d a
MRC Anxiety Disorders Unit, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa Maastricht University, Maastricht, The Netherlands Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa d Department of Psychiatry, Stellenbosch University, Cape Town, South Africa b c
a r t i c l e i n f o
abstract
Article history: Received 5 December 2012 Accepted 29 January 2013 Available online 5 March 2013
Background: The DSM-IV diagnosis of acute stress disorder (ASD) describes a posttraumatic reaction that occurs two to twenty-eight days following a trauma and involves symptoms of intrusion, avoidance, hyper-arousal and dissociation. A better understanding of ASD and its pathogenesis could lead to improved post-trauma health care interventions. The aim of this study was to determine prospectively whether a combination of clinical, cognitive and demographic variables were predictive of ASD severity in an acutely traumatized sample. Methods: We assessed demographic (e.g. age, gender, education), clinical (e.g. sleep quality, trait anxiety, previous psychiatric diagnoses), and cognitive (e.g. negative cognitions following trauma) variables in a sample of 125 adult motor vehicle accident survivors (age: 32.26 7 9.99; gender: 56.6% male) approximately 10 days after the accident. Univariate analyes and stepwise linear regression were performed to identify variables predictive of ASD severity. Results: Although a number of factors were individually associated with ASD severity, in a regression model only 3 factors, trait anxiety, suicide risk and post-traumatic cognitions, emerged as predictive of the severity of the disorder. Limitations: The cross-sectional nature of the study and use of self-report measures are important to bear in mind. Conclusions: Higher levels of trait anxiety, risk for suicide and negative appraisals of the traumatic event were predictive of ASD severity. As these factors may help to identify those who may be at risk of more severe responses after a traumatic event, and who may benefit from secondary prevention strategies, they should be assessed for in acute trauma survivors. & 2013 Elsevier B.V. All rights reserved.
Keywords: Acute stress disorder Predictors
0. Introduction A wide range of emotional, cognitive, biological and behavioral symptoms can follow in the aftermath of a traumatic incident (Isserlin et al., 2008). Acute stress disorder (ASD) is one such posttraumatic stress reaction that occurs two days to four weeks following a trauma (APA, 2000). The DSM-IV diagnostic criteria for ASD include re-experiencing, avoidance, hyper-arousal and dissociation (Bryant and Harvey, 1997). Symptoms of dissociation include numbing, depersonalization, dissociative amnesia, and reduced awareness (APA, 2000). The question of which factors are associated with ASD is key to understanding and treating this disorder (Fitzharris et al., 2006). Most research to date has examined predictors of PTSD or the relationship between ASD and PTSD (Bryant and Harvey, 1997; Classen et al.,
n
Corresponding author. Tel.:þ 27 21 938 9020. E-mail address: [email protected] (S. Suliman).
0165-0327/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jad.2013.01.041
1993; Hamanaka et al., 2006; Harvey and Bryant, 1998a, 1999a; Holeva et al., 2001). Relatively few studies have examined clinical and cognitive factors that may be associated with ASD. Those that have done so have highlighted a number of variables. One of these is disturbed sleep, which is a feature of both diagnoses of ASD and PTSD. Several studies have demonstrated that sleep may be severely disrupted following exposure to a traumatic event (see Pillar et al., 2000 for a review). While for most people, sleep disturbances are transient (Lavie, 2001), for others, they persist or worsen, typically occurring as a symptom manifestation of ASD and PTSD (Harvey et al., 2003). Severe stress has been noted to adversely affect sleep by decreasing its efficiency, length, and quality (Basta et al., 2007; Kim and Dimsdale, 2007), and a review of subjective sleep reports from patients with PTSD revealed disturbances in sleep-onset, sleep maintenance and the presence of nightmares (Harvey et al., 2003). Negative cognitions or interpretations of the trauma and its impact deserve consideration as they have been observed to predict the development of post traumatic stress symptoms
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following trauma (Dunmore et al., 1999; Ehlers et al., 1998; Harvey and Bryant, 2002; Kaysen et al., 2005; Nixon et al., 2008; Peleg and Shalev, 2006). However, findings have been mixed regarding the association of negative cognitive appraisals and ASD. Some investigators (Elsesser et al., 2009) have found that individuals with ASD display negative cognitions about a range of concerns after trauma exposure, while others have found no association (Ehring et al., 2008; Kleim et al., 2007). Temperamental characteristics may also be a risk factor for ASD. Trait anxiety is a relatively stable dispositional tendency to perceive stressful situations as dangerous or threatening, and to respond with short-term elevations in current anxiety (Spielberger, 1983). It may be a vulnerability factor for a wide range of anxiety disorders, including PTSD. Individuals with a diagnosis of PTSD generally have higher trait anxiety compared to non-PTSD controls (Orsillo et al., 1996; Casada and Roache, 2005), with some studies suggesting that trait anxiety is a stronger determinant of PTSD symptom severity than the nature of the traumatic event (Lonigan et al., 1994; Phipps et al., 2009). Resilience’ describes the ability to adapt in the face of adversity or to bounce back from challenges or setbacks, and is one of several factors that can influence how individuals respond to stress (Connor, 2006; Connor and Davidson, 2003; Yehuda, 2004). Resilience and aspects of resilience, such as perception of stress, have been identified as risk/protective factors against the development of PTSD (Bonanno, 2004; King et al., 1998). Studies that have examined the correlates of acute stress responses following traumatic events have found that pre-morbid psychological disorders (Barton et al., 1996; Harvey and Bryant, 1998b), including prior history of PTSD, and co-morbid depression (Harvey and Bryant, 1998a, 1999a), as well as demographic variables, such as age (Harvey and Bryant, 1998a) and gender (Kangas et al., 2002) were significant correlates of ASD. If distinguishing clinical and cognitive factors for ASD can be identified, this may be useful in tailoring post-trauma interventions. A better understanding of ASD and the factors which are inherent to it might also prevent the disorder from ultimately developing into PTSD. Previous studies have tended to focus on factors that identify the presence/absence of ASD. The purpose of this study was to extend existing knowledge of clinical and cognitive factors involved in ASD severity. The primary aim of the study was to determine whether sleep, negative cognitions, trait anxiety, perceived stress and resilience/coping are predictive of ASD severity in an acutely traumatized sample, while accounting for other clinical and demographic variables. On the basis of available evidence we expected poorer sleep, lower resilience, and higher levels of trait anxiety, perceived stress and negative cognitions to be predictive of ASD severity.
1. Methods Ethics approval for the study was obtained from the University of Stellenbosch Health Research Ethics Committee and the hospital departments where recruitment took place. Assessments were conducted by trained researchers approximately 10 days (mean 10.3þ/ 4.6 days) after a motor vehicle accident (MVA). Written informed consent was obtained from all participants prior to inclusion in the study. 1.1. Participants Participants included 125 adult MVA survivors who were recruited from emergency and orthopedic units at four Cape Town hospitals. The following inclusion criteria were used: (i) willing and able to provide written informed consent; (ii) between the
ages of 18 and 65 years; (iii) able to read and write in English at 5th grade level; medically well enough to undergo testing. Exclusion criteria included: (i) current or past history of schizophrenia, bipolar disorder or other psychotic disorder as defined by the MINI; (ii) any significant recent or previous head injury (defined as a loss of consciousness for over 30 min and/or posttraumatic amnesia) or mild traumatic brain injury (mTBI); (iii) use of any psychotropic medication at the initial assessment; (iv) serious physical injury at inclusion where injury sequelae would interfere with study participation. 1.2. Assessment The following measures were administered to participants: Demographic questionnaire: This captures participant characteristics such as, age, gender, level of education, marital status, living arrangements, employment, medical and psychiatric history, as well as prior and current medication use. Abbreviated Injury Scale (AIS): This is an anatomical scoring system that provides a reasonably accurate way of ranking the severity of injury. Injuries are ranked on a scale of 1 to 6, with 1 being minor, 5 severe and 6 an un-survivable injury (AAAM, 1990). Acute Stress Disorder Scale (ASDS): This self-report inventory, based on DSM-IV criteria for ASD, contains 19 items that relate to ASD symptoms, and provides a total score of ASD severity (ranging from 19–95). A score of 56 and above indicates probable ASD. The ASDS possesses good sensitivity (95%), and specificity (83%) relative to a diagnosis of ASD on the Acute Stress Disorder Interview (Bryant et al., 2000). Connor-Davidson Resilience Scale (CD-RISC): This is a 25-item self-report measure with demonstrated reliability and validity that assesses stress-coping ability. Items including the ability to adapt to change or to bounce back from challenges and the extent of the individuals social network are measured on a 5-point scale ranging from 0 ‘not true at all’ to 5 ‘true nearly all of the time’. The CD-RISC provides a total score that ranges from 0–100, with higher scores indicating greater resilience (Connor and Davidson, 2003). MINI International Neuropsychiatric Interview (MINI): This is a clinician administered structured diagnostic interview based on DSM-IV diagnostic criteria that was used to assess for major psychiatric disorders. The MINI has been shown to have high validity and reliability scores when compared with results from the Structured Clinical Interview for DSM-III-R (SCID), the Composite International Diagnostic Interview (CIDI), the Diagnostic Interview Schedule (DIS) and the Present Status Examination (PSE) (Sheehan et al., 1998). Perceived Stress Scale (PSS): This is the most widely used instrument for measuring the perception of stress. It is a measure of the degree to which situations in one’s life are appraised as stressful. Items were designed to tap how unpredictable, uncontrollable, and overloaded respondents find their lives (Cohen et al., 1983; Cohen and Williamson, 1988). Pittsburg Sleep Quality Index (PSQI): This contains 19 selfrated questions and 5 rated by the bed-partner or room-mate if one is available (Buysse et al., 1989). Only the self-rated questions are used in the scoring. They are combined to form 7 component scores of 0–3 points each (0 ¼no difficulty; 3 ¼severe difficulty). The 7 component scores are then added to yield one global score with a range of 0-21 (difficulty; difficulty in all areas). A score of above 5 indicates a poor sleeper. High internal consistency, test–retest reliability, sensitivity and specificity have been found. Post Traumatic Cognitions Inventory (PTCI): The PTCI is a 36 item measure that is used to assess trauma-related cognitions, using a
S. Suliman et al. / Journal of Affective Disorders 149 (2013) 277–281
7-point Likert-type scale (Foa et al., 1999). The measure consists of 3 factors: negative cognitions about self, negative cognitions about the world, and self-blame. The 3 factors have shown excellent internal consistency and good test–retest reliability. They also correlate moderately to strongly with measures of PTSD severity. Spielberger State-Trait Anxiety Inventory-trait version (STAI-T): The STAI is a self-rated scale that measures current anxiety symptoms on a scale of 1 to 4 (1¼almost never; 4¼almost always). The trait version, which measures a more general and longstanding anxiety, as opposed to temporary state anxiety was used. A score of Z40 is considered ‘high anxiety’ (Spielberger, 1983). 1.3. Data-analyses Bivariate correlations were computed between continuous ASD scores and continuous variables, and t-tests or ANOVAs between continuous ASD scores and categorical variables. Clinical, cognitive and demographic variables that emerged as significant were entered into a stepwise linear regression model to determine their relationship with ASD severity. Level of significance was set at 0.05 and all tests were 2-tailed. The data was analyzed using SPSS for windows, version 18.0.
(MINI suicide risk; po0.001), marital status (p¼0.033), employment (p¼0.026) and use of psychiatric medications (p¼ 0.012) were significantly associated with ASDS scores. These variables were then entered into a multiple regression model to determine the strength of their associations with ASD score (Table 3). In the stepwise model only 3 variables were retained. Model 1: STAI-T; Model 2: STAI-T and MINI suicide risk; Model 3: STAI-T, MINI suicide risk and PTCI. STAI-T was the strongest predictor, accounting for a variance of 27.8% (adjusted r2 ¼ 0.278). This increased to 34.6% (adjusted r2 ¼0.346) when suicide risk was included in the model and to 38.4% (adjusted r2 ¼ 0.384) when PTCI total score was included. At all steps these predictors were highly significant (p o0.001). Anxiety and negative cognitions about the trauma had significantly positive regression weights indicating that participants with higher scores were more likely to have higher ASDS scores, after controlling for other variables in the model. Suicide
Table 2 Demographic and clinical variables, test (t-test, ANOVA, correlation) and level of significance to ASDS. Test
Variable
Test co-efficient
Significance
Correlation
Age Education STAI-T PSS CD-RISC PTCI PSQI
0.035 0.062 0.528 0.497 0.035 0.516 0.327
0.714 0.516 0.000 0.000 0.709 0.000 0.000
T-test
Gender Employment Religiosity Medical history Psychiatric history Psychiatric medication AIS Current depressiona Suicide riska
1.635 2.253 0.016 1.002 1.693 2.565 1.422 3.025 4.989
0.105 0.026 0.189 0.319 0.093 0.012 0.158 0.003 0.000
ANOVA
Ethnicity Marital status Living arrangements Income
0.970 2.722 1.878 1.154
0.410 0.033 0.119 0.337
2. Results Demographic details of the sample are given in Table 1. Participants had a mean age of 32.26 ( 79.99) years. The majority were male (56.8%), married or living with a partner (65.1%), of mixed race (45.2%), employed (75.6%) and Christian (82.9%). They had an average number of 9.95 ( 72.89) years of education of and 61.5% earned above R60000 ($8500) per annum. Participants were most commonly car passengers (29.7%) or pedestrians (27.6%) at the time of the accident and the severity of the injury was classified as minor in 52.1% of cases and moderate in 45.5% of cases. Most had no medical or psychiatric history (57.8% and 88.6% respectively), and 12.1% had previously used psychiatric medication. 40.9% of participants met criteria for probable ASD using the recommended cut-off of 56 and above. On univariate analyses (Table 2), negative appraisals of the trauma (PTCI; po0.001), level of sleep associated difficulties (PSQI; po0.001), trait anxiety (STAI-T; po0.001), perceived stress (PSS; po0.001), current depression (MINI MDD; p¼0.030), suicide risk
279
a All other variables assessed on the MINI were not found to be significantly associated.
Table 1 Demographic details of sample (N ¼125). Age Gender Marital status Ethnicity Education Religion Employment Income Accident type Injury severitya Medical history ASD Medication use Psychiatric history Psychiatric medication Time since MVA
32.26 7 9.99 Male: 56.8%; female: 43.2% Married/co-habiting: 65.1%; single: 25.2%; divorced/widowed: 9.8% Colored/mixed race: 45.2%; black: 36.3%; white: 14.5%; other: 4.0% Average number of years: 9.95 years 72.89 Christian: 82.9%; muslim: 6.6%; none: 4.9%; other: 3.3% Employed: 75.6%; unemployed: 13.4% 0–R59,000: 38.5%; Z R60,000 ($8500): 61.5% Car driver: 23.6%; car passenger: 29.7%; motorbike driver: 14.6%; pedestrian: 27.6%; bicycle:0.8% Minor: 52.1%; moderate: 45.5% Yes: 42.2%; no: 57.8% Yes (score of Z 56 on ASDS): 40.9%; no: 59.1% Yes: 76.5%; no: 13.5% Yes: 11.4%; no: 88.6% Yes: 12.1%; no: 87.9% 10.37 4.6 days
Note: percentages may not add up to 100 due to missing data. a
Assessed using the Abbreviated Injury Scale.
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Table 3 Predictors of ASDS score. Model
R2
Adjusted R2 F (df)
1 0.284 0.278 STAI-T 2 STAI-T Suicide 0.358 0.346 3 0.401 0.384 STAI-T Suicide PTCI
p-Value Muliple regression weights b
ß
0.901
0.533
0.734 12.096
0.435 0.290
0.530 9.627 0.133
0.314 0.230 0.256
42.896 (1, 108) 0.000 29.887 (2, 107) 0.000
23.650 (3, 106) 0.000
Stepwise regression: Model 1: Trait anxiety (STAI-T). Model 2: Trait anxiety and suicide risk. Model 3: Trait anxiety, suicide risk and negative cognitions (PTCI).
risk (coded as: 1¼yes, 2¼no) had a significant negative weight indicating that, after controlling for other variables in the model, those who were at risk for suicide were more likely to have a higher ASDS score.
3. Discussion When assessed individually, more negative appraisals of the trauma, poorer sleep, more perceived stress, higher levels of trait anxiety, meeting criteria for current depression, being at risk for suicide, not being married or employed and having used psychiatric medications in the past were all associated with higher ASD scores. However, when evaluated together in a regression model with all other factors held constant, only trait anxiety, suicide risk and trauma appraisal were retained as significant predictors of ASD severity with trait anxiety, accounting for 27.8% of the variance, being the most highly predictive (Model 1). When suicide risk was entered into the model 34.6% of the variance was accounted for (Model 2) and when trauma appraisal was included 38.4% of the variance was accounted for (Model 3). These findings were supported by the fact that these three variables had the strongest associations with ASD severity in univariate analyses. Trait anxiety was the most highly predictive of ASD severity in this sample. This is consistent with other research that has noted that individuals with a predisposition to anxiety problems are prone to developing clinically significant stress responses in relation to life stressors (Barton et al., 1996; Franklin and Zimmerman, 2001; Kangas et al., 2002; Yehuda, 1999). Thus, it is possible that close examination of temperamental variables associated with ASD may lead to better predictions of risk and vulnerability. Previous research has found that pre-morbid psychological problems prior to trauma exposure is a risk factor for the development of PTSD (Barton et al., 1996; Kangas et al., 2002) making the finding that suicide risk, albeit mild, is a predictor of ASD severity not surprising. On the other hand, depression has commonly been found to be comorbid with anxiety disorders (Blanchard et al., 1998; Shalev et al., 1998) and ASD (Harvey and Bryant, 1998b, 1999b), but we did not find this to be predictive of ASD severity in the regression analyses. However, Harvey and colleagues note that depressive symptoms may be a consequence of trauma exposure rather than a vulnerability factor (Harvey and Bryant, 1999b). Although dysfunctional cognitions have been found to be predictive of PTSD (Ehring et al., 2008; Kleim et al., 2007), their
relationship with ASD is controversial (Elsesser et al., 2009). It has been suggested that this could be due to the fact that ASD participants are often compared to trauma exposed participants without ASD, rather than to no-trauma exposed controls (Elsesser et al., 2009). The advantage of this study is that we looked at dysfunctional cognitive appraisals of the trauma and its sequelae in relation to ASD severity in a sample of trauma exposed participants. Thus, findings of this study indicate a strong association between ASD severity and negative cognitions when controlling for other variables. Contrary to expectation, coping behavior was not found to be associated with ASD severity. However, the assessment of this so soon after trauma exposure may not have allowed for effects on coping responses to develop. Sleep disturbances were also not found to be associated with ASD severity when other variables were taken into account, although there was a high level of sleep disturbances in the sample as a whole, indicating that this could be related to the trauma itself. It has been noted that despite the negative effects of poor sleep, such as increased irritability, anger, depression, deterioration of some cognitive performances, and daytime drowsiness, sleep deprivation after acute exposure to aversive events might assume an adaptive function (Kuriyama et al., 2010), by decreasing fear generalization for example (Mellman et al., 1998; North and Smith, 1990). Also, contrary to previous studies (Stein et al., 1997; Wolfe et al., 1999) we did not find an association between demographic variables (e.g. gender and age) and ASD severity. The cross sectional nature of the study is a limitation, as participants were only recruited into the study after a trauma. Future studies could improve on this by assessing risk/protective factors prior to trauma exposure and follow participants up longitudinally. The use of a self-report measure of ASD severity is also a limitation. The ASDS has, however, been found to have good sensitivity and specificity relative to a diagnosis of ASD on the Acute Stress Disorder Interview (Bryant et al., 2000). A strength of the study is the relatively homogenous group of trauma (MVA) survivors. However, caution should be used when applying the findings to other trauma populations. In conclusion, the strongest predictors of ASD severity were trait anxiety, suicide risk, and trauma appraisal. Given that a large proportion of individuals who develop ASD go on to experience longer lasting problems such as PTSD, these factors should be assessed for in acute trauma survivors in order to better identify those who may be at risk for PTSD and other psychiatric disorders (e.g. depression), as secondary preventive interventions may be helpful if timeously implemented. In this regard, longitudinal studies assessing pre-trauma vulnerability factors associated with ASD may be particularly insightful.
Role of funding source This study was funded by grants from the Stellenbosch University Faculty of Health Sciences, Hendrik Vrouwes Research Scholarship, and South African National Research Foundation (Thuthuka). Funders played no further role in the study.
Conflict of interest The authors have no conflicts of interest to disclose. Ms Suliman has received research grants from the Stellenbosch University Faculty of Health Sciences, Hendrik Vrouwes Research Scholarship, and South African National Research Foundation (Thuthuka). Prof. Stein has received research grants and/or consultancy honoraria from Abbott, Astrazeneca, Eli-Lilly, GlaxoSmithKline, Jazz Pharmaceuticals, Johnson & Johnson, Lundbeck, Orion, Pfizer, Pharmacia, Roche, Servier, Solvay, Sumitomo, Takeda, Tikvah, and Wyeth. Prof Seedat is supported by South African Research Chairs Initiative (SARChI) hosted by the Department of Science and Technology and the National Research Foundation, South Africa.
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Acknowledgments The authors wish to thank Ms Tracy Jacobs and Sr Marina Basson for their assistance with data collection, and Prof Martin Kidd for his statistical assistance.
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research report
Predictors of acute stress disorder severity Sharain Suliman a,n, Zyrhea Troeman b, Dan J. Stein a,c, Soraya Seedat a,d a
MRC Anxiety Disorders Unit, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa Maastricht University, Maastricht, The Netherlands Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa d Department of Psychiatry, Stellenbosch University, Cape Town, South Africa b c
a r t i c l e i n f o
abstract
Article history: Received 5 December 2012 Accepted 29 January 2013 Available online 5 March 2013
Background: The DSM-IV diagnosis of acute stress disorder (ASD) describes a posttraumatic reaction that occurs two to twenty-eight days following a trauma and involves symptoms of intrusion, avoidance, hyper-arousal and dissociation. A better understanding of ASD and its pathogenesis could lead to improved post-trauma health care interventions. The aim of this study was to determine prospectively whether a combination of clinical, cognitive and demographic variables were predictive of ASD severity in an acutely traumatized sample. Methods: We assessed demographic (e.g. age, gender, education), clinical (e.g. sleep quality, trait anxiety, previous psychiatric diagnoses), and cognitive (e.g. negative cognitions following trauma) variables in a sample of 125 adult motor vehicle accident survivors (age: 32.26 7 9.99; gender: 56.6% male) approximately 10 days after the accident. Univariate analyes and stepwise linear regression were performed to identify variables predictive of ASD severity. Results: Although a number of factors were individually associated with ASD severity, in a regression model only 3 factors, trait anxiety, suicide risk and post-traumatic cognitions, emerged as predictive of the severity of the disorder. Limitations: The cross-sectional nature of the study and use of self-report measures are important to bear in mind. Conclusions: Higher levels of trait anxiety, risk for suicide and negative appraisals of the traumatic event were predictive of ASD severity. As these factors may help to identify those who may be at risk of more severe responses after a traumatic event, and who may benefit from secondary prevention strategies, they should be assessed for in acute trauma survivors. & 2013 Elsevier B.V. All rights reserved.
Keywords: Acute stress disorder Predictors
0. Introduction A wide range of emotional, cognitive, biological and behavioral symptoms can follow in the aftermath of a traumatic incident (Isserlin et al., 2008). Acute stress disorder (ASD) is one such posttraumatic stress reaction that occurs two days to four weeks following a trauma (APA, 2000). The DSM-IV diagnostic criteria for ASD include re-experiencing, avoidance, hyper-arousal and dissociation (Bryant and Harvey, 1997). Symptoms of dissociation include numbing, depersonalization, dissociative amnesia, and reduced awareness (APA, 2000). The question of which factors are associated with ASD is key to understanding and treating this disorder (Fitzharris et al., 2006). Most research to date has examined predictors of PTSD or the relationship between ASD and PTSD (Bryant and Harvey, 1997; Classen et al.,
n
Corresponding author. Tel.:þ 27 21 938 9020. E-mail address: [email protected] (S. Suliman).
0165-0327/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jad.2013.01.041
1993; Hamanaka et al., 2006; Harvey and Bryant, 1998a, 1999a; Holeva et al., 2001). Relatively few studies have examined clinical and cognitive factors that may be associated with ASD. Those that have done so have highlighted a number of variables. One of these is disturbed sleep, which is a feature of both diagnoses of ASD and PTSD. Several studies have demonstrated that sleep may be severely disrupted following exposure to a traumatic event (see Pillar et al., 2000 for a review). While for most people, sleep disturbances are transient (Lavie, 2001), for others, they persist or worsen, typically occurring as a symptom manifestation of ASD and PTSD (Harvey et al., 2003). Severe stress has been noted to adversely affect sleep by decreasing its efficiency, length, and quality (Basta et al., 2007; Kim and Dimsdale, 2007), and a review of subjective sleep reports from patients with PTSD revealed disturbances in sleep-onset, sleep maintenance and the presence of nightmares (Harvey et al., 2003). Negative cognitions or interpretations of the trauma and its impact deserve consideration as they have been observed to predict the development of post traumatic stress symptoms
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following trauma (Dunmore et al., 1999; Ehlers et al., 1998; Harvey and Bryant, 2002; Kaysen et al., 2005; Nixon et al., 2008; Peleg and Shalev, 2006). However, findings have been mixed regarding the association of negative cognitive appraisals and ASD. Some investigators (Elsesser et al., 2009) have found that individuals with ASD display negative cognitions about a range of concerns after trauma exposure, while others have found no association (Ehring et al., 2008; Kleim et al., 2007). Temperamental characteristics may also be a risk factor for ASD. Trait anxiety is a relatively stable dispositional tendency to perceive stressful situations as dangerous or threatening, and to respond with short-term elevations in current anxiety (Spielberger, 1983). It may be a vulnerability factor for a wide range of anxiety disorders, including PTSD. Individuals with a diagnosis of PTSD generally have higher trait anxiety compared to non-PTSD controls (Orsillo et al., 1996; Casada and Roache, 2005), with some studies suggesting that trait anxiety is a stronger determinant of PTSD symptom severity than the nature of the traumatic event (Lonigan et al., 1994; Phipps et al., 2009). Resilience’ describes the ability to adapt in the face of adversity or to bounce back from challenges or setbacks, and is one of several factors that can influence how individuals respond to stress (Connor, 2006; Connor and Davidson, 2003; Yehuda, 2004). Resilience and aspects of resilience, such as perception of stress, have been identified as risk/protective factors against the development of PTSD (Bonanno, 2004; King et al., 1998). Studies that have examined the correlates of acute stress responses following traumatic events have found that pre-morbid psychological disorders (Barton et al., 1996; Harvey and Bryant, 1998b), including prior history of PTSD, and co-morbid depression (Harvey and Bryant, 1998a, 1999a), as well as demographic variables, such as age (Harvey and Bryant, 1998a) and gender (Kangas et al., 2002) were significant correlates of ASD. If distinguishing clinical and cognitive factors for ASD can be identified, this may be useful in tailoring post-trauma interventions. A better understanding of ASD and the factors which are inherent to it might also prevent the disorder from ultimately developing into PTSD. Previous studies have tended to focus on factors that identify the presence/absence of ASD. The purpose of this study was to extend existing knowledge of clinical and cognitive factors involved in ASD severity. The primary aim of the study was to determine whether sleep, negative cognitions, trait anxiety, perceived stress and resilience/coping are predictive of ASD severity in an acutely traumatized sample, while accounting for other clinical and demographic variables. On the basis of available evidence we expected poorer sleep, lower resilience, and higher levels of trait anxiety, perceived stress and negative cognitions to be predictive of ASD severity.
1. Methods Ethics approval for the study was obtained from the University of Stellenbosch Health Research Ethics Committee and the hospital departments where recruitment took place. Assessments were conducted by trained researchers approximately 10 days (mean 10.3þ/ 4.6 days) after a motor vehicle accident (MVA). Written informed consent was obtained from all participants prior to inclusion in the study. 1.1. Participants Participants included 125 adult MVA survivors who were recruited from emergency and orthopedic units at four Cape Town hospitals. The following inclusion criteria were used: (i) willing and able to provide written informed consent; (ii) between the
ages of 18 and 65 years; (iii) able to read and write in English at 5th grade level; medically well enough to undergo testing. Exclusion criteria included: (i) current or past history of schizophrenia, bipolar disorder or other psychotic disorder as defined by the MINI; (ii) any significant recent or previous head injury (defined as a loss of consciousness for over 30 min and/or posttraumatic amnesia) or mild traumatic brain injury (mTBI); (iii) use of any psychotropic medication at the initial assessment; (iv) serious physical injury at inclusion where injury sequelae would interfere with study participation. 1.2. Assessment The following measures were administered to participants: Demographic questionnaire: This captures participant characteristics such as, age, gender, level of education, marital status, living arrangements, employment, medical and psychiatric history, as well as prior and current medication use. Abbreviated Injury Scale (AIS): This is an anatomical scoring system that provides a reasonably accurate way of ranking the severity of injury. Injuries are ranked on a scale of 1 to 6, with 1 being minor, 5 severe and 6 an un-survivable injury (AAAM, 1990). Acute Stress Disorder Scale (ASDS): This self-report inventory, based on DSM-IV criteria for ASD, contains 19 items that relate to ASD symptoms, and provides a total score of ASD severity (ranging from 19–95). A score of 56 and above indicates probable ASD. The ASDS possesses good sensitivity (95%), and specificity (83%) relative to a diagnosis of ASD on the Acute Stress Disorder Interview (Bryant et al., 2000). Connor-Davidson Resilience Scale (CD-RISC): This is a 25-item self-report measure with demonstrated reliability and validity that assesses stress-coping ability. Items including the ability to adapt to change or to bounce back from challenges and the extent of the individuals social network are measured on a 5-point scale ranging from 0 ‘not true at all’ to 5 ‘true nearly all of the time’. The CD-RISC provides a total score that ranges from 0–100, with higher scores indicating greater resilience (Connor and Davidson, 2003). MINI International Neuropsychiatric Interview (MINI): This is a clinician administered structured diagnostic interview based on DSM-IV diagnostic criteria that was used to assess for major psychiatric disorders. The MINI has been shown to have high validity and reliability scores when compared with results from the Structured Clinical Interview for DSM-III-R (SCID), the Composite International Diagnostic Interview (CIDI), the Diagnostic Interview Schedule (DIS) and the Present Status Examination (PSE) (Sheehan et al., 1998). Perceived Stress Scale (PSS): This is the most widely used instrument for measuring the perception of stress. It is a measure of the degree to which situations in one’s life are appraised as stressful. Items were designed to tap how unpredictable, uncontrollable, and overloaded respondents find their lives (Cohen et al., 1983; Cohen and Williamson, 1988). Pittsburg Sleep Quality Index (PSQI): This contains 19 selfrated questions and 5 rated by the bed-partner or room-mate if one is available (Buysse et al., 1989). Only the self-rated questions are used in the scoring. They are combined to form 7 component scores of 0–3 points each (0 ¼no difficulty; 3 ¼severe difficulty). The 7 component scores are then added to yield one global score with a range of 0-21 (difficulty; difficulty in all areas). A score of above 5 indicates a poor sleeper. High internal consistency, test–retest reliability, sensitivity and specificity have been found. Post Traumatic Cognitions Inventory (PTCI): The PTCI is a 36 item measure that is used to assess trauma-related cognitions, using a
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7-point Likert-type scale (Foa et al., 1999). The measure consists of 3 factors: negative cognitions about self, negative cognitions about the world, and self-blame. The 3 factors have shown excellent internal consistency and good test–retest reliability. They also correlate moderately to strongly with measures of PTSD severity. Spielberger State-Trait Anxiety Inventory-trait version (STAI-T): The STAI is a self-rated scale that measures current anxiety symptoms on a scale of 1 to 4 (1¼almost never; 4¼almost always). The trait version, which measures a more general and longstanding anxiety, as opposed to temporary state anxiety was used. A score of Z40 is considered ‘high anxiety’ (Spielberger, 1983). 1.3. Data-analyses Bivariate correlations were computed between continuous ASD scores and continuous variables, and t-tests or ANOVAs between continuous ASD scores and categorical variables. Clinical, cognitive and demographic variables that emerged as significant were entered into a stepwise linear regression model to determine their relationship with ASD severity. Level of significance was set at 0.05 and all tests were 2-tailed. The data was analyzed using SPSS for windows, version 18.0.
(MINI suicide risk; po0.001), marital status (p¼0.033), employment (p¼0.026) and use of psychiatric medications (p¼ 0.012) were significantly associated with ASDS scores. These variables were then entered into a multiple regression model to determine the strength of their associations with ASD score (Table 3). In the stepwise model only 3 variables were retained. Model 1: STAI-T; Model 2: STAI-T and MINI suicide risk; Model 3: STAI-T, MINI suicide risk and PTCI. STAI-T was the strongest predictor, accounting for a variance of 27.8% (adjusted r2 ¼ 0.278). This increased to 34.6% (adjusted r2 ¼0.346) when suicide risk was included in the model and to 38.4% (adjusted r2 ¼ 0.384) when PTCI total score was included. At all steps these predictors were highly significant (p o0.001). Anxiety and negative cognitions about the trauma had significantly positive regression weights indicating that participants with higher scores were more likely to have higher ASDS scores, after controlling for other variables in the model. Suicide
Table 2 Demographic and clinical variables, test (t-test, ANOVA, correlation) and level of significance to ASDS. Test
Variable
Test co-efficient
Significance
Correlation
Age Education STAI-T PSS CD-RISC PTCI PSQI
0.035 0.062 0.528 0.497 0.035 0.516 0.327
0.714 0.516 0.000 0.000 0.709 0.000 0.000
T-test
Gender Employment Religiosity Medical history Psychiatric history Psychiatric medication AIS Current depressiona Suicide riska
1.635 2.253 0.016 1.002 1.693 2.565 1.422 3.025 4.989
0.105 0.026 0.189 0.319 0.093 0.012 0.158 0.003 0.000
ANOVA
Ethnicity Marital status Living arrangements Income
0.970 2.722 1.878 1.154
0.410 0.033 0.119 0.337
2. Results Demographic details of the sample are given in Table 1. Participants had a mean age of 32.26 ( 79.99) years. The majority were male (56.8%), married or living with a partner (65.1%), of mixed race (45.2%), employed (75.6%) and Christian (82.9%). They had an average number of 9.95 ( 72.89) years of education of and 61.5% earned above R60000 ($8500) per annum. Participants were most commonly car passengers (29.7%) or pedestrians (27.6%) at the time of the accident and the severity of the injury was classified as minor in 52.1% of cases and moderate in 45.5% of cases. Most had no medical or psychiatric history (57.8% and 88.6% respectively), and 12.1% had previously used psychiatric medication. 40.9% of participants met criteria for probable ASD using the recommended cut-off of 56 and above. On univariate analyses (Table 2), negative appraisals of the trauma (PTCI; po0.001), level of sleep associated difficulties (PSQI; po0.001), trait anxiety (STAI-T; po0.001), perceived stress (PSS; po0.001), current depression (MINI MDD; p¼0.030), suicide risk
279
a All other variables assessed on the MINI were not found to be significantly associated.
Table 1 Demographic details of sample (N ¼125). Age Gender Marital status Ethnicity Education Religion Employment Income Accident type Injury severitya Medical history ASD Medication use Psychiatric history Psychiatric medication Time since MVA
32.26 7 9.99 Male: 56.8%; female: 43.2% Married/co-habiting: 65.1%; single: 25.2%; divorced/widowed: 9.8% Colored/mixed race: 45.2%; black: 36.3%; white: 14.5%; other: 4.0% Average number of years: 9.95 years 72.89 Christian: 82.9%; muslim: 6.6%; none: 4.9%; other: 3.3% Employed: 75.6%; unemployed: 13.4% 0–R59,000: 38.5%; Z R60,000 ($8500): 61.5% Car driver: 23.6%; car passenger: 29.7%; motorbike driver: 14.6%; pedestrian: 27.6%; bicycle:0.8% Minor: 52.1%; moderate: 45.5% Yes: 42.2%; no: 57.8% Yes (score of Z 56 on ASDS): 40.9%; no: 59.1% Yes: 76.5%; no: 13.5% Yes: 11.4%; no: 88.6% Yes: 12.1%; no: 87.9% 10.37 4.6 days
Note: percentages may not add up to 100 due to missing data. a
Assessed using the Abbreviated Injury Scale.
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Table 3 Predictors of ASDS score. Model
R2
Adjusted R2 F (df)
1 0.284 0.278 STAI-T 2 STAI-T Suicide 0.358 0.346 3 0.401 0.384 STAI-T Suicide PTCI
p-Value Muliple regression weights b
ß
0.901
0.533
0.734 12.096
0.435 0.290
0.530 9.627 0.133
0.314 0.230 0.256
42.896 (1, 108) 0.000 29.887 (2, 107) 0.000
23.650 (3, 106) 0.000
Stepwise regression: Model 1: Trait anxiety (STAI-T). Model 2: Trait anxiety and suicide risk. Model 3: Trait anxiety, suicide risk and negative cognitions (PTCI).
risk (coded as: 1¼yes, 2¼no) had a significant negative weight indicating that, after controlling for other variables in the model, those who were at risk for suicide were more likely to have a higher ASDS score.
3. Discussion When assessed individually, more negative appraisals of the trauma, poorer sleep, more perceived stress, higher levels of trait anxiety, meeting criteria for current depression, being at risk for suicide, not being married or employed and having used psychiatric medications in the past were all associated with higher ASD scores. However, when evaluated together in a regression model with all other factors held constant, only trait anxiety, suicide risk and trauma appraisal were retained as significant predictors of ASD severity with trait anxiety, accounting for 27.8% of the variance, being the most highly predictive (Model 1). When suicide risk was entered into the model 34.6% of the variance was accounted for (Model 2) and when trauma appraisal was included 38.4% of the variance was accounted for (Model 3). These findings were supported by the fact that these three variables had the strongest associations with ASD severity in univariate analyses. Trait anxiety was the most highly predictive of ASD severity in this sample. This is consistent with other research that has noted that individuals with a predisposition to anxiety problems are prone to developing clinically significant stress responses in relation to life stressors (Barton et al., 1996; Franklin and Zimmerman, 2001; Kangas et al., 2002; Yehuda, 1999). Thus, it is possible that close examination of temperamental variables associated with ASD may lead to better predictions of risk and vulnerability. Previous research has found that pre-morbid psychological problems prior to trauma exposure is a risk factor for the development of PTSD (Barton et al., 1996; Kangas et al., 2002) making the finding that suicide risk, albeit mild, is a predictor of ASD severity not surprising. On the other hand, depression has commonly been found to be comorbid with anxiety disorders (Blanchard et al., 1998; Shalev et al., 1998) and ASD (Harvey and Bryant, 1998b, 1999b), but we did not find this to be predictive of ASD severity in the regression analyses. However, Harvey and colleagues note that depressive symptoms may be a consequence of trauma exposure rather than a vulnerability factor (Harvey and Bryant, 1999b). Although dysfunctional cognitions have been found to be predictive of PTSD (Ehring et al., 2008; Kleim et al., 2007), their
relationship with ASD is controversial (Elsesser et al., 2009). It has been suggested that this could be due to the fact that ASD participants are often compared to trauma exposed participants without ASD, rather than to no-trauma exposed controls (Elsesser et al., 2009). The advantage of this study is that we looked at dysfunctional cognitive appraisals of the trauma and its sequelae in relation to ASD severity in a sample of trauma exposed participants. Thus, findings of this study indicate a strong association between ASD severity and negative cognitions when controlling for other variables. Contrary to expectation, coping behavior was not found to be associated with ASD severity. However, the assessment of this so soon after trauma exposure may not have allowed for effects on coping responses to develop. Sleep disturbances were also not found to be associated with ASD severity when other variables were taken into account, although there was a high level of sleep disturbances in the sample as a whole, indicating that this could be related to the trauma itself. It has been noted that despite the negative effects of poor sleep, such as increased irritability, anger, depression, deterioration of some cognitive performances, and daytime drowsiness, sleep deprivation after acute exposure to aversive events might assume an adaptive function (Kuriyama et al., 2010), by decreasing fear generalization for example (Mellman et al., 1998; North and Smith, 1990). Also, contrary to previous studies (Stein et al., 1997; Wolfe et al., 1999) we did not find an association between demographic variables (e.g. gender and age) and ASD severity. The cross sectional nature of the study is a limitation, as participants were only recruited into the study after a trauma. Future studies could improve on this by assessing risk/protective factors prior to trauma exposure and follow participants up longitudinally. The use of a self-report measure of ASD severity is also a limitation. The ASDS has, however, been found to have good sensitivity and specificity relative to a diagnosis of ASD on the Acute Stress Disorder Interview (Bryant et al., 2000). A strength of the study is the relatively homogenous group of trauma (MVA) survivors. However, caution should be used when applying the findings to other trauma populations. In conclusion, the strongest predictors of ASD severity were trait anxiety, suicide risk, and trauma appraisal. Given that a large proportion of individuals who develop ASD go on to experience longer lasting problems such as PTSD, these factors should be assessed for in acute trauma survivors in order to better identify those who may be at risk for PTSD and other psychiatric disorders (e.g. depression), as secondary preventive interventions may be helpful if timeously implemented. In this regard, longitudinal studies assessing pre-trauma vulnerability factors associated with ASD may be particularly insightful.
Role of funding source This study was funded by grants from the Stellenbosch University Faculty of Health Sciences, Hendrik Vrouwes Research Scholarship, and South African National Research Foundation (Thuthuka). Funders played no further role in the study.
Conflict of interest The authors have no conflicts of interest to disclose. Ms Suliman has received research grants from the Stellenbosch University Faculty of Health Sciences, Hendrik Vrouwes Research Scholarship, and South African National Research Foundation (Thuthuka). Prof. Stein has received research grants and/or consultancy honoraria from Abbott, Astrazeneca, Eli-Lilly, GlaxoSmithKline, Jazz Pharmaceuticals, Johnson & Johnson, Lundbeck, Orion, Pfizer, Pharmacia, Roche, Servier, Solvay, Sumitomo, Takeda, Tikvah, and Wyeth. Prof Seedat is supported by South African Research Chairs Initiative (SARChI) hosted by the Department of Science and Technology and the National Research Foundation, South Africa.
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Acknowledgments The authors wish to thank Ms Tracy Jacobs and Sr Marina Basson for their assistance with data collection, and Prof Martin Kidd for his statistical assistance.
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