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Injury journal homepage: www.elsevier.com/locate/injury
Review
A systematic review of predictors of posttraumatic stress disorder (PTSD) for adult road traffic crash survivors Michelle Heron-Delaney a,*, Justin Kenardy a, Erin Charlton a, Yutaka Matsuoka b a School of Medicine, Centre of National Research on Disability and Rehabilitation Medicine (CONROD), University of Queensland, Royal Brisbane and Women’s Hospital, Level 1 Edith Cavell Building, Herston, QLD 4029, Australia b Department of Clinical Epidemiology, Translational Medical Center, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan
A R T I C L E I N F O
A B S T R A C T
Article history: Accepted 9 July 2013
Posttraumatic stress disorder (PTSD) is one of the most common psychological consequences for adult road traffic crash (RTC) survivors and can have serious and long-lasting consequences for recovery if left untreated. Prevalence rates of PTSD following a RTC vary from 6% to 45% (based on 51 prevalence estimates across 35 studies). Explanations for this wide variance are explored. A systematic review of published studies found 49 papers (44 unique studies) investigating predictors of later PTSD in RTC survivors. Consistent predictors of PTSD include rumination about the trauma, perceived threat to life, a lack of social support, higher Acute Stress Disorder symptom severity, persistent physical problems, previous emotional problems, previous anxiety disorder and involvement in litigation/compensation. Moderate predictors of PTSD are discussed, as well as factors, which consistently do not predict PTSD in RTC survivors. The results inform future models of post-RTC traumatic stress aetiology. Crown Copyright ß 2013 Published by Elsevier Ltd. All rights reserved.
Keywords: Posttraumatic stress disorder PTSD Road traffic crash Predictors Prevalence
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inclusion/exclusion criteria . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PTSD prevalence rates following RTC. . . . . . . . . . . Predictors of PTSD for RTC survivors . . . . . . . . . . . Consistency of predictors of PTSD . . . . . . . . . . . . . Influence of study characteristics on predictors of Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prevalence rates of PTSD . . . . . . . . . . . . . . . . . . . . Predictors of PTSD . . . . . . . . . . . . . . . . . . . . . . . . . Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implications and summary. . . . . . . . . . . . . . . . . . . Conflict of interest statement . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Introduction Road traffic crashes (RTCs) can have serious and long-lasting consequences for survivors, both in terms of physical and
* Corresponding author. Tel.: +61 7 3346 4790; fax: +31 7 3346 4603. E-mail address:
[email protected] (M. Heron-Delaney).
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psychological outcomes [1,2]. Each year, a vast number of people are involved in RTCs. In 2008 there were 1,630,000 road traffic crashes in the USA, which involved personal injury, which resulted in 2,346,000 injured survivors [3]. Understanding the prevalence and course of development of psychological impairment is necessary in order to provide the most effective and timely intervention. A significant proportion of RTC survivors will develop psychological disorders following a RTC [4]. The most common
0020–1383/$ – see front matter . Crown Copyright ß 2013 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.injury.2013.07.011
Please cite this article in press as: Heron-Delaney M, et al. A systematic review of predictors of posttraumatic stress disorder (PTSD) for adult road traffic crash survivors. Injury (2013), http://dx.doi.org/10.1016/j.injury.2013.07.011
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disorders following RTCs are posttraumatic stress disorder (PTSD), major depressive disorder, driving phobias and other anxiety disorders [5]. While mental health consequences are often overlooked or discounted due to the primary focus of physical health outcomes, previous research indicates that the consequences of psychological disorders following injury can be longlasting [6], and that psychological and physical outcomes influence one another to impact on recovery [7,8]. The current review will focus on prevalence estimates and predictors of posttraumatic stress disorder (PTSD) following RTC-related trauma. PTSD was selected as the focus due to the large body of research investigating PTSD following RTC, compared with other psychological outcomes such as depression and anxiety, which is sparse. Furthermore, PTSD is one of the most common psychological consequences for RTC survivors and RTCs are the leading cause of PTSD in the general population [9]. PTSD, as defined by DSM-IV-TR [10], is characterised by intrusive thoughts (e.g., flashbacks or nightmares), avoidance (of stimuli associated with the trauma) and hyperarousal (e.g., difficulty falling asleep, anger) symptoms that occur in response to experiencing a serious or life-threatening event. The diagnosis of PTSD requires that symptoms be present for at least four weeks and that these symptoms cause significant impairment in the individual’s ability to lead a normal life. The focus on RTC-related trauma follows from concerns that unique problems may be associated with specific types of traumatic events. For example, traumatic events that are characterised as involving intention to injure (assaults) are distinct from those with no intent. Furthermore there are a wide range of different types of unintentional injuries (e.g., work-related injuries, sporting accidents). Different types of unintentional trauma involve distinct factors, which may influence PTSD symptom aetiology. For example, sporting injuries are unlikely to be caused by negligence and result in litigation and compensation processes, whereas RTC-related trauma frequently involve negligence and results in litigation and/or compensation processes. Having a more homogenous sample of trauma (specifically RTC-related) helps focus our analysis of predictors of PTSD and the effect predictors have on outcomes. Whilst generalizability may be constrained, the motivation for adopting this approach is that it provides greater precision in linking trauma and circumstances of trauma to outcome.
Inclusion/exclusion criteria To be included in the PTSD prevalence (see Table 1) and predictors review (see Table 2), the following criteria needed to be met: the sample was 100% RTC survivors, PTSD was assessed using DSM-III, DSM-III-R or DSM-IV criteria, assessment of PTSD occurred at four weeks or later (in line with DSM-IV criteria) and the follow-up time point was narrowly defined, i.e., spanning no more than four months. This excluded samples, which provided a broad time-frame for follow-up, for example, 3–12 months postRTC. Studies were excluded if they specifically examined populations with potentially confounding factors such as traumatic brain injury, post-traumatic amnesia, or previous history of trauma. Paediatric populations were also excluded. Case studies and dissertations were excluded, as were papers in languages other than English where no translation was available. If the same cohort was utilised in multiple papers, the paper with the largest sample size was included. There were minor variations in inclusion/exclusion criteria for the review of prevalence rates and predictors of PTSD. (1) To be included in the prevalence review, prevalence estimates of PTSD needed to be provided or calculable based on data reported. Thus PTSD diagnosis needed to be reported (rather than symptom severity). For the predictor review, PTSD diagnosis or symptom severity measures were allowable. If the study assessed both, we focused on predictors of the PTSD formal diagnosis rather than symptom severity. If only predictors of PTSD symptom severity (as opposed to PTSD diagnosis) were reported (N = 12 studies), these results were included. (2) In both reviews, if studies made a distinction between a PTSD group and a PTSD and sub-syndromal PTSD group then the second group was ignored. However, in five studies no distinction was made between full PTSD versus PTSD plus sub-syndromal PTSD when analysing predictors of PTSD. These predictors were included in the predictor review; however combined samples of PTSD and sub-syndromal PTSD were not included in the prevalence review. (3) To be included in predictor review, papers needed to assess predictors of PTSD. Specifically, studies needed to include predictors of onset of PTSD, as opposed to maintenance, remission or delayed onset of PTSD. Table 1 is arranged by study and ordered chronologically according to the first time point when PTSD prevalence is reported for a given study. Table 2 is organised by predictor. At least three studies needed to measure a given predictor variable for it to be included in Table 2.
Method The following databases were searched using the keywords ‘‘motor vehicle’’, ‘‘road traffic accident’’ or ‘‘road traffic crash’’ and ‘‘PTSD’’ or ‘‘stress’’ in any field: Psych Info, Pub Med, PILOT, ProQuest research library/social science journals, CINAHL, Health source (Nursing/Academic Edition), Medline, Scopus, Web of Science and Cochrane Library. This resulted in 1945 unique records (publications could be from any time period contained in these databases, with the most recent publication date of August 2012). All abstracts were read thoroughly and articles were only eliminated at this stage if they failed to meet inclusion criteria (i.e., referred to non-RTC trauma or paediatric populations, etc.). In total 1739 articles were eliminated, resulting in 206 papers, which were retrieved and read in full due to their potential relevance to provide information on prevalence rates or predictors of PTSD in RTC survivors. The reference lists of the retrieved 206 articles were searched, and this provided two unique articles that had not been found via database searches. A search of Google Scholar did not result in any new articles. In total, 35 papers met inclusion criteria (see below) for our review of PTSD prevalence estimates following RTC (Table 1). Forty-nine papers (44 unique studies) were utilised in the table of predictors (Table 2).
Results PTSD prevalence rates following RTC Prevalence rates of PTSD following a RTC varied considerably across studies, ranging from 6% to 45% (based on 51 prevalence estimates across 35 studies; see Table 1). A range of factors (e.g., country of study, sample size, time point of PTSD assessment, injury severity and the measure used to assess PTSD) could account for the wide range of PTSD prevalence estimates. We attempted to determine the influence of individual factors on prevalence outcome. When examined by time point of PTSD assessment, prevalence rates presented in Table 1 showed small variations. At one month post-RTC, prevalence estimates ranged from 8% to 45% (median = 27.0), at three months 8–30% (median = 16.5), at six months 6–28% (median = 18.0) and at twelve months 7–26% (median = 14.0). These ranges demonstrate an overall decreasing trend in PTSD prevalence estimates over time, which may reflect a natural remission in PTSD symptoms between 1 month and 12 months post RTC, consistent with previous research [11,12].
Please cite this article in press as: Heron-Delaney M, et al. A systematic review of predictors of posttraumatic stress disorder (PTSD) for adult road traffic crash survivors. Injury (2013), http://dx.doi.org/10.1016/j.injury.2013.07.011
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Table 1 Prevalence rates for PTSD in road traffic crash survivors. Study
Country
N
Time point
Injury severity
Measure to assess PTSD and DSM version
PTSD prevalence (%)
Delahanty [38] Ongecha-Owuor [40] Fullerton [42] Matsuoka [44] Coronas [14]
USA Africa USA Japan Spain
59 264 122 100 119 108
1 month 1 month 1 month 4–6 weeks 1 month 4 months
Serious Serious Serious Severe Serious
SCID (DSM-IV [39]) SPI (DSM-IV [41]) SCID (DSM-III-R [43]) CAPS (DSM-IV [25]) SCID (DSM-III-R [39])
20 13 34 8 45 33
Ursano [11]
USA
122 99 99 86 86
1 3 6 9 1
month months months months year
Serious
SCID (DSM-III-R [43])
34 25 18 17 14
Irish [45]
USA
weeks months months months year
CAPS (DSM-IV [25])
Australia
6 6 3 6 1
Hospitalized
Jeavons [30]
196 196 72 62 58
Mild, moderate & severe
PTSD-I (DSM-III-R [46])
10 7 8 8 9
Yasan [12]
Turkey
84 78 67
3 months 6 months 1 year
Attended ED
CAPS (DSM-IV [25])
30 23 18
Mayou [47]
UK
174
3 months
Minor & Major
Diagnostic criteria for PTSD (DSM-III-R [48])
8
171
1 year
8
Ehlers [29]
UK
888 781
3 months 1 year
None, mild & moderate
PSS (DSM-IV [49])
23 17
Mayou [1]
UK
111 111 111
3 months 1 year 5 years
Attended ED
PSS (DSM-IV [49])
10 7 8
Smith [31] Blanchard [51] Holeva [52]
UK USA UK
39 158 265
4 months 4 months 4–6 months
Minor (out-patients) Sought medical attention Serious
13 39 23
Hamanaka [54] Ehring [32] Frommberger [55]
Japan UK Germany
82 140 152
6 months 6 months 6 months
Bryant [57] Harvey & Bryant [59] Kuhn [60] Chossegros [62] McFarlane [64] Berna [65] Fuglsang [66]
Australia Australia Germany France Australia France Denmark
113 71 58 541 26 155 90
6 months 6 months 6 months 6 months 6 months 6 months 6–8 months
Serious Moderate to severe Hospitalized, minimum of bone fracture Hospitalized > 24 h Hospitalized > 24 h Moderate to Severe Hospitalized Hospitalized Hospitalized Attended ED
SRS-PTSD (DSM-III-R [50]) SCID (DSM-III-R [43]) Penn Inventory (DSM edition not specified [53]) SCID (DSM-IV [39]) SCID (DSM-IV [39]) ADIS-R (DSM-III-R [56]) CIDI-PTSD (DSM-III-R [58]) CIDI-PTSD (DSM-III-R [58]) SCID (DSM-IV German version [61]) PCL (DSM-IV [63]) CAPS (DSM-IV [25]) CAPS (DSM-IV [25]) PDS (DSM-IV [67])
21 25 6 18 27 8 17
Ryb [68]
USA
367
6 months
Hospitalized
Diagnostic criteria for PTSD (DSM-IV [69])
28
317
1 year
Bryant [70]
Australia
87 87
6 months 2 years
Hospitalized
CIDI-PTSD (DSM-III-R [58])
22 23
Chan [71] Blanchard [72] Koren [73] Green [74] Silove [76] Mayou & Bryant [5] Blanchard [77] a Blanchard [78] a
Australia USA Israel Australia Australia UK USA USA
355 132 102 24 83 507 68 75
9 months 1 year 1 year 1.5 years 1.5 years 3 years 13 months 21 months
Minor & severe Sought medical attention Hospitalized, mild to moderate Hospitalized, serious Hospitalized 76% Minor, 24% moderate Hospitalized Hospitalized
PCL (DSM-IV [63]) CAPS (DSM-IV [25]) SCID (DSM-III-R [43]) DIS (DSM-III-R [75]) CIDI-Auto (DSM-III-R [58]) PSS (DSM-IV [49]) CAPS (DSM-IV [25]) CAPS (DSM-IV [25])
29 14 26 25 22 11 72 68
a
9 12 18
24
Treatment seeking populations (not included in analyses due to potential to inflate PTSD estimates).
Prevalence estimates varied according to the country where the research was conducted: 7–39% for the US, 7–23% for the UK, 8– 29% for Australia, and 6–45% for all other countries combined. A possible explanation for this variance is that cultural and socioeconomic differences between countries influence prevalence rates for PTSD following traumatic injury [13] (see discussion below). Sample size also appeared to influence PTSD prevalence estimates: 6–45% for samples less than 200, 13–29% for samples
between 200 and 499 and 11–23% for samples of 500 or more participants. It was not possible to assess whether or not severity of injury predicted prevalence estimates, because the range of injuries was restricted to mostly severe injuries (injury severity was determined based on the study authors’ description of injury severity for each study). However, previous research suggests that injury severity does not influence the likelihood of developing PTSD
Please cite this article in press as: Heron-Delaney M, et al. A systematic review of predictors of posttraumatic stress disorder (PTSD) for adult road traffic crash survivors. Injury (2013), http://dx.doi.org/10.1016/j.injury.2013.07.011
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Table 2 Predictors of PTSD following a RTC (listed by predictor).
Female gender
Age > 30 years
Significant predictor
N.S.
Ongecha-Owuor [40] (1 month), Ursano [11] (1 month), Dougall [79] (1 and 6 months), Blanchard [80] (1–4 months), Coronas [81] (2 months), Ehlers [29] (3 months), Chossegros [62] (6 months), Frommberger [55] (6 months), Ryb [68] (6 months and 1 year), Yasan [12] (1 year), Mayou [17] (3 years) Ryb [68] (6 months and 1 year)
Coronas [14] (1 and 4 months), Matsuoka [44] (4–6 weeks), Ursano [11] (3 and 6 months), Yasan [12] (3 and 6 months), Bryant [57] (6 months), Jeavons [82] (3 and 6 months), Holeva [52] (4–6 months), Ehring [16] (6 months), Hamanaka [54] (6 months), Fuglsang [66] (6–8 months), Dougall [79] (1 year), Ehlers [29] (1 year), Bryant [70] (2 years) Delahanty [83] (1 month), Ursano [11] (1, 3 and 6 months), Coronas [14] (1 and 4 months), Dougall [79] (1 and 6 months and 1 year), Blanchard [80] (1–4 months), Irish [45] (6 weeks and 6 months), Ehlers [29] (3 months and 1 year), Jeavons [82] (3 and 6 months), Holeva [52] (4–6 months), Chossegros [62] (6 months), Fuglsang [66] (6–8 months), Bryant [57] (6 months), Chan [71] (9 months), Bryant [70] (2 years) Dougall [79] (1 and 6 months), Ursano [11] (6 months) Dougall [79] (1 and 6 months and 1 year), Blanchard [80] (1–4 months), Coronas [81] (2 months), Jeavons [30] (3 and 6 months), Ryb [68] (6 months and 1 year), Chan [71] (9 months) Delahanty [83] (1 month), Ursano [11] (1, 3 and 6 months), Dougall [79] (1 and 6 months and 1 year), Coronas [81] (2 months), Yasan [12] (3 and 6 months and 1 year) Delahanty [83] (1 month), Ursano [11] (1, 3 and 6 months), Dougall [79] (1 month and 1 year), Blanchard [80] (1–4 months), Matsuoka [44] (4–6 weeks), Coronas [81] (2 months), Yasan [12] (3 and 6 months and 1 year), Ryb [68] (6 months and 1 year), Chan [71] (9 months) Ongecha-Owuor [40] (1 month), Blanchard [80] (1–4 months), Ehlers [29] (3 months and 1 year), Chossegros [62] (6 months), Ehring [16] (6 months), Ryb [68] (6 months and 1 year), Koren [15] (1 year)
Minority ethnicity Relationship status
Ursano [11] (1 and 3 months), Blanchard [80] (1–4 months)
Lower income
Irish [45] (6 weeks and 6 months)
Lower education level
Dougall [79] (6 months)
Non-driver position
Matsuoka [44] (4–6 weeks)
Fatality from RTC
Ongecha-Owuor [40] (1 month), Blanchard [80] (1–4 months), Ryb [68] (6 months and 1 year)
Previous RTC Two-wheel vehicle
Chossegros [62] (6 months)
Four-wheel vehicle
Ehring [32] (1, 3 and 6 months), Frommberger [55] (6 months) Blanchard [80] (1–4 months), Matsuoka [44] (4–6 weeks), Coronas [81] (2 months), Coronas [14] (4 months), Chossegros [62] (6 months), Hamanaka [54] (6 months), Frommberger [55] (6 months), Mayou [84] (1 year)
High injury severity
Low injury severity High blood pressure
Dougall [79] (1 month), Delahanty [87] (1 month) Systolic: Coronas [14] (1 and 4 months); Diastolic: Coronas [14] (1 month)
Low blood pressure
Diastolic: Ehring [16] (6 months); Blanchard [77] (13 months) Matsuoka [44] (4–6 weeks), Coronas [14] (1 and 4 months), Bryant [57] (6 months), Kuhn [88] (6 months), Bryant [70] (2 years) Blanchard [77] (13 months) Ongecha-Owuor [40] (1 month) Ehring [32] (1, 3 and 6 months), Hamanaka [54] (6 months), Mayou [84] (1 year), Mayou [17] (3 years), Mayou [1] (5 years) Chossegros [62] (6 months), Ryb [68] (6 months), Delahanty [89] (6 months and 1 year), Mayou [84] (1 year) Yasan [12] (3 and 6 months), Holeva [52] (4–6 months), Bryant [57] (6 months), Hamanaka [54] (6 months), Fuglsang [66] (6–8 months), Bryant [70] (2 years) Nishi [90] (1 month), Jeavons [82] (6 months), Mayou [17] (3 years), Mayou [1] (5 years) Ehlers [29] (3 months and 1 year), Mayou [84] (1 year), Mayou [17] (3 years) Ursano [11] (3 months), Ehring [32] (1, 3 and 6 months), Yasan [12] (3 months), Delahanty [38] (1 month), Murray [85] (1 and 6 months), Irish [45] (6 weeks and 6 months), Ehlers [29] (3 months and 1 year), Ehring [16] (6 months), Holeva [91] (4–6 months), Kuhn [88] (1 month)
Elevated heart rate
Low heart rate Previous health problems Persistent physical problems at 3 months post-RTC Perception of not responsible for RTC ASD/higher ASD symptom severity Initial emotional distress Anger (3 months post-RTC) Peritraumatic dissociation
Blanchard [80] (1–4 months), Ehlers [29] (3 months and 1 year), Holeva [52] (4–6 months), Koren [15] (1 year) Ongecha-Owuor [40] (1 month), Ehring [16] (6 months), Mayou [84] (1 year)
Coronas [14] (1 month), Ehring [32] (1, 3 and 6 months), Murray [85] (1 and 6 months), Irish [45] (6 weeks and 6 months), Jeavons [82] (3 and 6 months), Jeavons [30] (3 and 6 months and 1 year), Ehlers [29] (3 months and 1 year), Holeva [52] (4–6 months), Bryant [57] (6 months), Dorfel [86] (6 months), Ehring [16] (6 months), Ryb [68] (6 months and 1 year), Dougall [79] (6 months and 1 year), Fuglsang [66] (6–8 months), Koren [15] (1 year), Green [74] (1.5 years), Bryant [70] (2 years), Mayou [17] (3 years) Diastolic: Coronas [14] (4 months); Systolic: Ehring [16] (6 months), Blanchard [77] (13 months); Systolic and Diastolic: Bryant [57] (6 months), Hamanaka [54] (6 months), Bryant [70] (2 years)
Ehring [16] (6 months), Hamanaka [54] (6 months), Ryb [68] (6 months and 1 year), Kuhn [88] (1 and 3 months)
Ehlers [29] (3 months and 1 year), Mayou [17] (3 years) Ehlers [29] (3 months and 1 year)
Blanchard [80] (1–4 months), Jeavons [82] (3 and 6 months), Koren [15] (1 year), Ryb [68] (1 year), Mayou [1] (5 years) Yasan [12] (1 year)
Jeavons [82] (3 months), McFarlane [64] (6 months), Green [74] (1.5 years) Ehring [16] (6 months) Yasan [12] (6 months and 1 year), McFarlane [64] (6 months), Kuhn [88] (3 and 6 months), Mayou [17] (3 years)
Please cite this article in press as: Heron-Delaney M, et al. A systematic review of predictors of posttraumatic stress disorder (PTSD) for adult road traffic crash survivors. Injury (2013), http://dx.doi.org/10.1016/j.injury.2013.07.011
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5
Table 2 (Continued )
Trauma rumination
Number of past trauma/prior trauma
Unconscious at RTC Conscious at RTC No amnesia at RTC Amnesia at RTC Previous emotional problems Prior psychiatric illness
Significant predictor
N.S.
Ehring [32] (1, 3 and 6 months), Murray [85] (1 and 6 months), Mayou [17] (3 years), Ehring [92] (1, 3 and 6 months), Mayou [84] (1 year) Delahanty [87] (1 month), Ryb [68] (6 months and 1 year)
–
Blanchard [80] (1–4 months), Mayou [93] (3 months), Ryb [68] (6 months) Mayou [47] (1 year), Mayou [1] (5 years) Flesher [94] (1 month) Chossegros [62] (6 months) Ehring [32] (1 and 6 months), Ehlers [29] (3 months and 1 year), Ehring [16] (6 months), Mayou [84] (1 year) Ongecha-Owuor [40] (1 month), Jeavons [82] (6 months), Chossegros [62] (6 months)
Prior PTSD
Delahanty [83] (1 month), Ursano [11] (1 and 3 months)
Prior anxiety
Blanchard [80] (1–4 months), Ursano [11] (3 and 6 months), Frommberger [55] (6 months), Ryb [68] (6 months) Blanchard [80] (1–4 months), Frommberger [55] (6 months), Blanchard [78] (13–16 months)
Prior depression Prior alcohol or drug abuse Prior Axis 1 disorder Prior Axis 2 disorder
Blanchard [80] (1–4 months), Blanchard [78] (1–2 years) Ursano [11] (3 and 6 months)
Prior family psychiatric illness Hospital admission
Ehlers [29] (3 months and 1 year)
Longer stay in hospital
Frommberger [55] (6 months), Jeavons [30] (1 year)
Lack of social support
Ehring [32] (1, 3 and 6 months), Yasan [12] (3 and 6 months and 1 year), Dougall [79] (6 months and 1 year), Clapp [95] (2.9 years) Delahanty [87] (1 month), Ehring [32] (1, 3 and 6 months), Dougall [79] (1 and 6 months and 1 year), Matsuoka [44] (4–6 weeks), Blanchard [80] (1–4 months), Irish [45] (6 weeks and 6 months), Jeavons [30] (3 and 6 months), Ehlers [29] (3 months and 1 year), Mayou [84] (1 year), Ryb [68] (6 months and 1 year), Green [74] (1.5 years) Ehring [32] (1, 3 and 6 months), Ehring [16] (6 months) Blanchard [80] (1–4 months), Ehlers [29] (3 months and 1 year), Bryant [96] (2 years), Mayou [17] (3 years)
Perceived threat to life during RTC
Fear during RTC Involved in litigation/ compensation claim
[9,14–17] (see discussion on the utility of injury severity as a predictor of PTSD below). Predictors of PTSD for RTC survivors As indicated by the PTSD prevalence rates reported above, only a small to moderate proportion of individuals involved in a RTC develop PTSD. Most of the research in this area has investigated which factors are predictors of PTSD in RTC survivors. Table 2 provides a comprehensive review of predictors of PTSD in RTC samples. Consistency of predictors of PTSD To ascertain the consistency and utility of each variable identified as a predictor of PTSD (from Table 2), Fisher’s exact test analyses were conducted to calculate odds ratios. The proportion inputs required for analyses were calculated using the number of samples/time points (not the number of studies) that a variable significantly predicted PTSD, as a percentage of the total number of samples the variable was assessed as a predictor. Odds ratios were classified as having small (between 1.44 and 2.46), moderate (between 2.47 and 4.24) or large (4.25 and over)
Ursano [11] (1, 3 and 6 months), Ehring [32] (1, 3 and 6 months), Blanchard [80] (1–4 months), Matsuoka [44] (4–6 weeks), Coronas [81] (2 months), Jeavons [82] (3 and 6 months), Ehring [16] (6 months) Jeavons [82] (3 and 6 months), Ryb [68] (1 year), Mayou [93] (1 year), Mayou [17] (3 years) Jeavons [82] (3 and 6 months), Ryb [68] (1 year) Ehring [32] (3 months), Mayou [17] (3 years) Coronas [14] (1 and 4 months), Matsuoka [44] (4–6 weeks), Coronas [81] (2 months), Jeavons [82] (3 months), Mayou [1] (5 years) Delahanty [87] (1 month), Blanchard [80] (1–4 months), Ursano [11] (6 months) Ursano [11] (1 month), Ryb [68] (1 year) Blanchard [78] (1–2 years) Blanchard [80] (1–4 months), Ryb [68] (6 months and 1 year), Blanchard [78] (1–2 years), Mayou [1] (5 years) Blanchard [78] (13–16 months) Ursano [11] (1 month), Blanchard [80] (1–4 months), Blanchard [78] (1–2 years) Matsuoka [44] (4–6 weeks), Coronas [81] (2 months), [82] (3 and 6 months) Jeavons [82] (3 and 6 months), Chan [71] (9 months), Mayou [17] (3 years) Jeavons [30] (3 and 6 months), Bryant [57] (6 months), Bryant [70] (2 years) Dougall [79] (1 month)
Ehring [16] (6 months), Mayou [17] (3 years)
Fuglsang [66] (6–8 months) Koren [15] (1 year), Mayou [1] (5 years)
effect sizes based on the conversion system outlined in Chinn [18]. There were two possible options for determining the number of instances to be included in the review: (a) number of time points/ samples or (b) number of studies. We chose the former method. This meant a given predictor could be identified as significant at more than one time point (if this was the case). However, this was deemed more accurate than choosing ‘study’ as the unit of analysis, because this would mean not differentiating between studies where a given variable significantly predicted PTSD at four timepoints versus a study where the variable was only significant for one out four time-points. Exact tests were run for all predictor variables. Twelve variables had large effect sizes and were classified as consistent predictors of PTSD. These included: rumination about the trauma, perceived threat to life, lack of social support, higher Acute Stress Disorder (ASD) symptom severity, persistent physical problems, previous emotional problems (defined as the presence of PTSD, anxiety, depression, travel phobia or irritability prior to the RTC, assessed via a structured clinical interview or self-report, where the authors did not provide information on the impact of each disorder separately, but instead grouped all together as ‘‘previous emotional problems’’), previous anxiety disorder, and involvement in litigation/compensation. The remaining four predictors with
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Table 3 Predictors and non-predictors of PTSD following a RTC. Predictors of PTSD
Non-predictors of PTSD a
Rumination about the trauma Perceived threat to lifea Lack of social supporta
Higher ASD symptom severitya Persistent physical problemsa Previous emotional problemsa Previous anxiety disordera Involvement in litigation/compensationa Being exposed to a fatality during the RTCa Feelings of angera Previous depressiona Fear during the RTCa Peritraumatic dissociationb Previous axis 1 disorderb Initial emotional distressc
a b c
Heart rate Prior PTSD Perception of responsibility for the RTC Gender Ethnic group Previous axis II disorder Consciousness at the time of the RTC Blood pressure Previous psychiatric illness Length of stay in hospital Admission to hospital Previous health problems Amnesia during RTC Injury severity Number of past traumatic events Income Position in vehicle Age Education level Relationship status Previous RTC Previous alcohol or drug abuse Previous family psychiatric illness
Large effect size. Moderate effect size. Small effect size.
large effect sizes were assessed by six or less samples, and therefore should be interpreted with caution. These included: being exposed to a fatality during the RTC, feelings of anger, previous depression, and fear during the RTC. Although being exposed to a fatality during the RTC was only assessed by four samples, all found it to be predictive of PTSD, suggesting that this would remain a consistent predictor if assessed by more studies. Peritraumatic dissociation and previous Axis 1 disorder had moderate effect sizes, although the latter was based on only three samples and thus should be interpreted with caution. Initial emotional distress and type of vehicle (e.g., 4-wheel drive or motorcycle) had small effect sizes. While these variables do not consistently predict PTSD, they do have some utility. Heart rate and prior PTSD had effect sizes close to zero, indicating no utility in predicting PTSD. Perception of responsibility for the RTC, gender, ethnic group, previous Axis II disorder, consciousness at the time of the RTC, blood pressure, previous psychiatric illness, length of stay in hospital, admission to hospital, previous health problems, amnesia during RTC, injury severity, number of past traumatic events, income, position in vehicle, age, education level, relationship status, previous RTC, previous alcohol or drug abuse, and previous family psychiatric illness were all found to have effect sizes in the opposite direction, such that these factors consistently do not predict PTSD, or in other words have no utility in predicting PTSD. This list of predictors and non-predictors of PTSD is contained in Table 3 for easy reference. Influence of study characteristics on predictors of PTSD Additional exact test analyses were conducted to determine if study characteristics influenced whether variables were predictive of PTSD or not. For each variable that was identified as a potential predictor of PTSD, Fisher’s exact tests were conducted to compare the number of samples which found the variable to be predictive versus the number of samples which found the variable not to be predictive on the following characteristics: country where the
study was conducted; DSM diagnostic criterion (edition) used to diagnose PTSD; population characteristics; time points when PTSD was assessed; and sample size. To conduct these exact tests, each of the study characteristics were categorised (with a maximum of three categories allowable per characteristic). Country was classified into three categories, determined by where the study was conducted. For most predictor variables, the country categories were the UK, the USA, and other. DSM diagnostic criterion used to assess PTSD was dichotomised: DSM-III-R versus DSM-IV. Population was categorised as: Presented to the Emergency Department (ED), Hospitalized, or other. The time points at which PTSD was assessed were divided into three groups: One month, to assess initial PTSD; greater than one month and up to six months (inclusive), to assess short term PTSD; and greater than six months, to assess longer-term PTSD. Sample size was grouped into small (500) categories. These categories were determined by examining the frequency distribution of sample sizes from all studies that assessed predictors of PTSD. Exact tests were not conducted for variables where no variance was present (e.g., if all studies were conducted in the USA) or if all studies found a variable to be predictive (e.g., fatality from RTC) or not predictive of PTSD (e.g., relationship status) because it is not possible to conduct exact tests when no variance is present. If only one sample was found to be predictive or not predictive, while all other samples were consistent, exact tests were not run due to an inflated chance/bias towards finding a significant relationship between that one sample and one of the study characteristics. This could give an inflated estimate of the relevance of a study characteristic (e.g., country where the study was conducted) in determining the likelihood of a given sample predicting or not predicting PTSD. Exact tests were not run if the total number of samples assessing the variable was less than seven. A frequency distribution was examined to determine the distribution of the total number of samples that assessed each predictor variable. This distribution showed a distinct peak at seven samples, which we also judged as a large enough number of assessments to support the results as reliable. We wanted to avoid the possibility that a given variable appeared to interact with study characteristics to predict/not predict PTSD because of a spurious pattern due to the small number of samples assessing the variable. For variables with three categories (e.g., high blood pressure, low blood pressure, and non-significant) the smallest category (e.g., low blood pressure) was not included in the exact tests analyses. Exact tests were only conducted with dichotomous outcome variables (to reduce the level of complexity for interpretation), thus one category needs to be removed before analyses could be conducted. This culling of the third category occurred for six variables, and for each of these variables the smallest category contained only one or two samples, and this minority of samples reported results, which were contradictory to the majority of reported data for these variables. Ninety-one exact test analyses were conducted. Results indicated that the likelihood of a given sample predicting or not predicting PTSD was not related to country, measure, population, time point or sample size for female gender, age, initial emotional distress, high injury severity, minority ethnic group, elevated heart rate, perception of responsibility for RTC, consciousness at time of RTC, previous psychiatric illness, peritraumatic dissociation, perceived threat to life, previous emotional problems, previous anxiety disorders, involvement in litigation/compensation claim, blood pressure and persistent physical problems in the three months following the RTC. Past trauma and income level predicted PTSD in studies where the sample size was greater than 200, but did not predict PTSD when the sample size was less than 200. These results
Please cite this article in press as: Heron-Delaney M, et al. A systematic review of predictors of posttraumatic stress disorder (PTSD) for adult road traffic crash survivors. Injury (2013), http://dx.doi.org/10.1016/j.injury.2013.07.011
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need to be interpreted with extreme caution because only a small number of samples (two for income, three for past trauma) identified these variables as predictive of PTSD, while at least 11 samples indicate these variables do not predict PTSD. While we cannot rule this out as a genuine result, more studies would need to confirm this relationship between sample size and predictive utility for these variables before considering this a valid and meaningful result. Discussion Prevalence rates of PTSD This review indicates that prevalence rates of PTSD following a RTC vary considerably across studies, ranging from 6% to 45%. The vast majority of prevalence estimates of PTSD following RTC exceed the reported 12-month prevalence rate of 6.4% reported for the general population in Australia [19], and 3.5% reported in the USA [20]. Thus, experiencing a RTC increases the incidence of PTSD, relative to the general population. The variance in prevalence estimates was best explained by three factors: time point when PTSD was assessed (i.e., higher prevalence estimates at one month post-RTC than later), sample size and socioeconomic and cultural factors associated with the country where the study was conducted. This review found that larger sample sizes provided more conservative prevalence estimates of PTSD. Larger sample sizes may provide a more representative estimate of the true population prevalence because random error and imprecision in measurement are reduced. Furthermore, outliers have an undue influence on prevalence estimates in samples of less than 200 [21]. Matsuoka [13] investigated the potential influence of socioeconomic and cultural factors by examining the interaction between PTSD prevalence and infant mortality rate. Infant mortality rate is closely associated with a country’s level of health care, technological development and medical advances. It also relates to a country’s population density, ethnic background, founding history, dietary habit and residential setting. These authors compared the prevalence rates of PTSD following accident-related injury (predominantly RTC) from seven studies conducted across six developed countries. PTSD was assessed between 4 and 12 months post-accident, and prevalence rates displayed large variations between countries: 17–32% in the UK, USA and Israel, 10.4% in Australia, 8.5% in Japan, and 1.9–3.1% in Switzerland. Results indicated that those countries with lower infant mortality rates tended to have lower prevalence rates of PTSD. This suggests that cultural and socioeconomic factors may influence the prevalence of PTSD following traumatic events, and therefore may help explain the wide variance in prevalence estimates in the current review. Predictors of PTSD This review revealed that consistent predictors of PTSD for RTC survivors are previous emotional problems, having a previous anxiety disorder, perceived threat to life during the RTC, having a fatality occur during the RTC, rumination about the trauma, lack of social support, higher ASD symptom severity in the month following the RTC, persistent physical problems following the RTC and involvement in litigation/compensation. These findings indicate two key issues that warrant further discussion: The lack of support for injury severity as a predictor of PTSD and the strong support for involvement in litigation/compensation as a predictor of PTSD. There has been disagreement in the literature regarding whether or not injury severity predicts later PTSD [9,22,23]. The
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present review suggests that injury severity does not predict PTSD. Objective injury severity may not necessarily be indicative of whether an individual perceives their RTC to have been life threatening, which was found to be an important and consistent predictor of later PTSD. Thus it appears that it is not so much what happens to an individual, in terms of the severity of the injury, but instead how the individual perceives and processes the RTC that predicts development of PTSD. This is consistent with a cognitive model of PTSD [24]. There has been much controversy in the literature surrounding whether or not involvement in litigation/compensation predicts poorer health outcomes following injury (see Blake [25] and Carroll [26] for reviews on this topic). The present review indicates that being involved in litigation/the compensation process predicts development of PTSD. PTSD is one possible health outcome that can be assessed following RTC. Previous reviews have focused on other health outcomes such as physical disability, return to work and overall mental health without specifically focusing on PTSD. It seems plausible that involvement in litigation/compensation may increase the likelihood of developing PTSD due to the increased frequency of reminders of the RTC and the need to recount aspects of the trauma and continuing symptoms in what may be considered an unsupportive or stressful environment (i.e., with insurance managers or lawyers) [27]. Finally, other factors co-vary with likelihood to initiate litigation/the compensation process and these are not accounted for or described here because it is beyond the scope of this review. The nine consistent predictors of PTSD described above could be used to develop a screen for PTSD in individuals who have sustained injuries from RTCs. O’Donnell [28] created a screening tool for assessing PTSD in survivors of traumatic injury during hospitalization. This sample was predominantly RTC survivors (62%) however the remaining 38% included falls, assaults, workrelated accidents and other forms of traumatic injury, thus the sample was not RTC-specific. As this tool was designed for use during hospitalization (approximately 8 days post-injury) it covers factors that occurred before, during or in the week following the traumatic event. There are some similarities between O’Donnell’s screen and the variables identified as consistent predictors in the current review. Both identify previous emotional problems, perceived threat to life and lack of social support as important factors. The current review differs from O’Donnell’s screen by identifying higher ASD symptom severity, having a previous anxiety disorder, rumination about the trauma and having a fatality occur during the RTC as important predictors of subsequent PTSD. It seems unlikely that ASD symptom severity is uniquely associated with RTC-specific PTSD; however the other three factors may be uniquely associated with PTSD precipitated by RTC versus other types of trauma. It is also important to consider when it is most appropriate to screen for PTSD following RTC. The aforementioned factors can all be screened in the week following the RTC. However, this review also identified persistent physical problems following the RTC and involvement in litigation/ compensation as important predictors. A screen involving these items would need to be completed at least 1–2 months post-RTC. Future research Future research should endeavour to create items corresponding to the nine variables identified as consistent and important predictors of PTSD in RTC survivors, and validate these items in an RTC survivor sample, with the aim of developing a screening tool. The present review also identified four variables that had large effect sizes, and thus have the potential to be consistent predictors of PTSD, however, too few samples have analysed these variables to be sure of these results. It seems likely that these variables
Please cite this article in press as: Heron-Delaney M, et al. A systematic review of predictors of posttraumatic stress disorder (PTSD) for adult road traffic crash survivors. Injury (2013), http://dx.doi.org/10.1016/j.injury.2013.07.011
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would emerge as consistent predictors if more research were conducted. Having a fatality occur during the RTC consistently predicted the development of PTSD at the four time samples it was assessed. Previous depression is similar to previous emotional problems, which was a consistent predictor of PTSD. Fear during the RTC is similar to perceived threat to life, which consistently predicted PTSD. The final variable which appeared consistent, but had only been examined by a handful of studies was ‘anger’. This variable may prove useful as a predictor, as it was a significant predictor in O’Donnell’s screening tool in a general traumatic injury population. Thus these variables should be examined further in future research. The question of which specific cultural and socioeconomic factors influence prevalence of PTSD following traumatic events warrants further investigation to establish which factors are associated with lower levels of PTSD in RTC survivors. Larger sample sizes (i.e., greater than 500) should be recruited where possible, to avoid random error/imprecision in measurement and overestimation of PTSD prevalence rates. Finally, only a minority of published studies report effect sizes for predictors of PTSD in RTC samples. Future research should consistently report effect sizes. This review reveals that previous research has focused almost exclusively on RTC survivors suffering from serious and lifethreatening injuries. Very little research has investigated predictors of PTSD in RTC samples suffering from minor injuries. To date, the handful of studies which have [5,29–31], indicate that posttraumatic symptoms are experienced and can continue over an extended period of time if left untreated. More research is needed to clarify our understanding of predictors of PTSD in the large, but under-studied population of RTC survivors with minor injuries. Implications and summary This review indicates that a significant minority of individuals involved in a RTC will develop PTSD, which shows a need for identification and intervention to prevent the development of long-term disability. PTSD needs to be identified and treated as it is likely to have significant consequences for quality of life [32], absenteeism from work [33] and is associated with higher levels of pain and disability in RTC survivors if left untreated [8,34,35]. These negative consequences lead to substantial cost to both the individual and society. Most people receive treatment for physical injuries, however initial indications suggest only a minority receive treatment for mental health problems [36]. One barrier to treatment is identification of individuals who are suffering from PTSD following a RTC. This review identifies factors that predict which individuals will develop PTSD, and thus provides a starting point for developing a PTSD screening tool, which is specific to RTC survivors. Early identification of at-risk individuals would allow for early targeted intervention to facilitate optimal recovery from RTC-related injuries [37]. In summary, this review indicates that prevalence rates of PTSD in RTC samples vary widely and explanations were proposed to account for the wide variance in estimates. This review identified consistent and moderate predictors of PTSD, as well as factors, which consistently do not predict PTSD in RTC survivors.
Conflict of interest statement We do not have any financial or personal relationships with others that could inappropriately influence our work. We declare there are no conflicts of interest.
Acknowledgements This work was supported by funding from the Motor Accident Insurance Commission (MAIC). The funder had no input in study design; collection, analysis and interpretation of data, or the writing of the manuscript. The conclusions are those of the authors and do not necessarily reflect the views and opinions of the project funder. We thank two anonymous reviewers for their feedback on this manuscript. References [1] Mayou R, Tyndel S, Bryant B. Long-term outcome of motor vehicle accident injury. Psychosomatic Medicine 1997;59(6):578–84. [2] Bryant RA, Harvey AG. Avoidant coping style and post-traumatic stress following motor vehicle accidents. Behaviour Research and Therapy 1995;33(6):631–5. [3] Economic Commission for Europe. Statistics of road traffic accidents in Europe and North America. Geneva: United Nations; 2011. [4] Mayou R, Bryant B, Ehlers A. Prediction of psychological outcomes one year after a motor vehicle accident. 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