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Chapter 13 - Motor Vehicle Collisions

from Section 4 - Factors Influencing the Onset and Course of Posttraumatic Stress Disorder

Published online by Cambridge University Press:  26 July 2018

Evelyn J. Bromet
Affiliation:
State University of New York, Stony Brook
Elie G. Karam
Affiliation:
St George Hospital University Medical Center, Lebanon
Karestan C. Koenen
Affiliation:
Harvard University, Massachusetts
Dan J. Stein
Affiliation:
University of Cape Town
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Trauma and Posttraumatic Stress Disorder
Global Perspectives from the WHO World Mental Health Surveys
, pp. 194 - 209
Publisher: Cambridge University Press
Print publication year: 2018

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References

Amos, T., Stein, D. J., & Ipser, J. C. (2014). Pharmacological interventions for preventing post-traumatic stress disorder (PTSD). The Cochrane Database of Systematic Reviews, Issue 7, CD006239.CrossRefGoogle ScholarPubMed
Beck, J. G., & Coffey, S. F. (2007). Assessment and treatment of PTSD after a motor vehicle collision: empirical findings and clinical observations. Professional Psychology: Research and Practice, 38, 629–39.Google ScholarPubMed
Benjet, C., Bromet, E., Karam, E. G., et al. (2016). The epidemiology of traumatic event exposure worldwide: results from the World Mental Health Survey Consortium. Psychological Medicine, 46, 117.Google Scholar
Bisson, J. I. (2014). Early responding to traumatic events. British Journal of Psychiatry, 204, 329–30.CrossRefGoogle ScholarPubMed
Blanchard, E. B., & Hickling, E. J. (2004). After the Crash: Psychological Assessment and Treatment of Survivors of Motor Vehicle Accidents. Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Blaszcyzynski, A., Gordon, K., Silove, D., et al. (1998). Psychiatric morbidity following motor vehicle accidents: a review of methodological issues. Comprehensive Psychiatry, 39, 111–21.Google Scholar
Brewin, C. R. (2005a). Risk factor effect sizes in PTSD: what this means for intervention. Journal of Trauma & Dissociation, 6, 123–30.Google Scholar
Brewin, C. R. (2005b). Systematic review of screening instruments for adults at risk of PTSD. Journal of Traumatic Stress, 18, 5362.CrossRefGoogle ScholarPubMed
Brewin, C. R., Andrews, B., & Valentine, J. D. (2000). Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults. Journal of Consulting and Clinical Psychology, 68, 748–66.CrossRefGoogle ScholarPubMed
Clapp, J. D., Baker, A. S., Litwack, S. D., Sloan, D. M., & Beck, J. G. (2014). Properties of the Driving Behavior Survey among individuals with motor vehicle accident-related posttraumatic stress disorder. Journal of Anxiety Disorders, 28, 17.Google Scholar
Courvoisier, D. S., Combescure, C., Agoritsas, T., Gayet-Ageron, A., & Perneger, T. V. (2011). Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. Journal of Clinical Epidemiology, 64, 9931000.CrossRefGoogle ScholarPubMed
Craig, A., Tran, Y., Guest, R., et al. (2016). Psychological impact of injuries sustained in motor vehicle crashes: systematic review and meta-analysis. BMJ Open, 6, e011993.CrossRefGoogle ScholarPubMed
DiGangi, J. A., Gomez, D., Mendoza, L., et al. (2013). Pretrauma risk factors for posttraumatic stress disorder: a systematic review of the literature. Clinical Psychology Review, 33, 728–44.Google Scholar
Dohrenwend, B. P., Turner, J. B., Turse, N. A., et al. (2006). The psychological risks of Vietnam for U.S. veterans: a revisit with new data and methods. Science, 313, 979–82.Google Scholar
Forneris, C. A., Gartlehner, G., Brownley, K. A., et al. (2013). Interventions to prevent post-traumatic stress disorder: a systematic review. American Journal of Preventive Medicine, 44, 635–50.Google Scholar
Galatzer-Levy, I. R., Karstoft, K. I., Statnikov, A., & Shalev, A. Y. (2014). Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application. Journal of Psychiatric Research, 59, 6876.Google Scholar
Guest, R., Tran, Y., Gopinath, B., Cameron, I. D., & Craig, A. (2016). Psychological distress following a motor vehicle crash: a systematic review of preventative interventions. Injury, 47, 2415–23.Google Scholar
Hanley, J. A., & McNeil, B. J. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148, 839–43.CrossRefGoogle ScholarPubMed
Haro, J. M., Arbabzadeh-Bouchez, S., Brugha, T. S., et al. (2006). Concordance of the Composite International Diagnostic Interview Version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health surveys. International Journal of Methods in Psychiatric Research, 15, 167–80.Google Scholar
Harvey, A. G., & Bryant, R. A. (2000). Memory for acute stress disorder symptoms: a two-year prospective study. Journal of Nervous and Mental Disease, 188, 602–7.CrossRefGoogle ScholarPubMed
Heron-Delaney, M., Kenardy, J., Charlton, E., & Matsuoka, Y. (2013). A systematic review of predictors of posttraumatic stress disorder (PTSD) for adult road traffic crash survivors. Injury, 44, 1413–22.CrossRefGoogle ScholarPubMed
Karstoft, K. I., Galatzer-Levy, I. R., Statnikov, A., et al. (2015). Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry, 15, 30.Google Scholar
Kazantzis, N., Kennedy-Moffat, J., Flett, R. A., et al. (2012). Predictors of chronic trauma-related symptoms in a community sample of New Zealand motor vehicle accident survivors. Culture, Medicine and Psychiatry, 36, 442–64.CrossRefGoogle Scholar
Kenardy, J., Heron-Delaney, M., Warren, J., & Brown, E. (2015). The effect of mental health on long-term health-related quality of life following a road traffic crash: results from the UQ SuPPORT study. Injury, 46, 883–90.CrossRefGoogle ScholarPubMed
Kessler, R. C., McLaughlin, K. A., Green, J. G., et al. (2010). Childhood adversities and adult psychopathology in the WHO World Mental Health Surveys. British Journal of Psychiatry, 197, 378–85.CrossRefGoogle ScholarPubMed
Kessler, R. C., Rose, S., Koenen, K. C., et al. (2014). How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys. World Psychiatry, 13, 265–74.Google Scholar
Kliem, S., & Kroger, C. (2013). Prevention of chronic PTSD with early cognitive behavioral therapy. A meta-analysis using mixed-effects modeling. Behaviour Research and Therapy, 51, 753–61.CrossRefGoogle ScholarPubMed
Knäuper, B., Cannell, C. F., Schwarz, N., Bruce, M. L., & Kessler, R. C. (1999). Improving accuracy of major depression age-of-onset reports in the US National Comorbidity Survey. International Journal of Methods in Psychiatric Research, 8, 3948.Google Scholar
Korn, E., & Graubard, B. (1990). Simultaneous testing of regression coefficients with complex survey data: use of the Bonferroni t statistics. The American Statistician, 44, 270–6.Google Scholar
Kuch, K., Cox, B. J., & Evans, R. J. (1996). Posttraumatic stress disorder and motor vehicle accidents: a multidisciplinary overview. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie, 41, 429–34.Google Scholar
Lissek, S., & van Meurs, B. (2014). Learning models of PTSD: theoretical accounts and psychobiological evidence. International Journal of Psychophysiology, 98, 594605.CrossRefGoogle ScholarPubMed
Murray, C. J., Barber, R. M., Foreman, K. J., et al. (2015). Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. Lancet, 386, 2145–91.CrossRefGoogle ScholarPubMed
Nickerson, A., Aderka, I. M., Bryant, R. A., & Hofmann, S. G. (2013). The role of attribution of trauma responsibility in posttraumatic stress disorder following motor vehicle accidents. Depression and Anxiety, 30, 483–8.CrossRefGoogle ScholarPubMed
Norris, F. H. (1992). Epidemiology of trauma: frequency and impact of different potentially traumatic events on different demographic groups. Journal of Consulting and Clinical Psychology, 60, 409–18.CrossRefGoogle ScholarPubMed
O'Donnell, M. L., Creamer, M. C., Parslow, R., et al. (2008). A predictive screening index for posttraumatic stress disorder and depression following traumatic injury. Journal of Consulting and Clinical Psychology, 76, 923–32.Google Scholar
Ozer, E. J., Best, S. R., Lipsey, T. L., & Weiss, D. S. (2003). Predictors of posttraumatic stress disorder and symptoms in adults: a meta-analysis. Psychological Bulletin, 129, 5273.CrossRefGoogle ScholarPubMed
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49, 1373–79.CrossRefGoogle ScholarPubMed
Pratchett, L. C., & Yehuda, R. (2011). Foundations of posttraumatic stress disorder: does early life trauma lead to adult posttraumatic stress disorder? Development and Psychopathology, 23, 477–91.Google Scholar
Reed, J. F. I. (2007). Better binomial confidence intervals. Journal of Modern Applied Statistical Methods, 6, 153–61.Google Scholar
Rice, M. E., & Harris, G. T. (2005). Comparing effect sizes in follow-up studies: ROC Area, Cohen's d, and r. Law and Human Behavior, 29, 615–20.Google Scholar
Roemer, L., Litz, B. T., Orsillo, S. M., Ehlich, P. J., & Friedman, M. J. (1998). Increases in retrospective accounts of war-zone exposure over time: the role of PTSD symptom severity. Journal of Traumatic Stress, 11, 597605.CrossRefGoogle ScholarPubMed
SAS Institute Inc. (2008). SAS Software, Version 9.2. Cary, NC: SAS Institute Inc.Google Scholar
Smith, G. C., Seaman, S. R., Wood, A. M., Royston, P., & White, I. R. (2014). Correcting for optimistic prediction in small data sets. American Journal of Epidemiology, 180, 318–24.CrossRefGoogle ScholarPubMed
Stein, D. J., Boshoff, D., Traut, A., et al. (1997). Patients presenting with fresh trauma after interpersonal violence. Part II. Assault history. South African Medical Journal, 87, 9991000.Google ScholarPubMed
Stein, D. J., Williams, S. L., Jackson, P. B., et al. (2009). Perpetration of gross human rights violations in South Africa: association with psychiatric disorders. South African Medical Journal, 99, 390–5.Google Scholar
Steyerberg, E. W., Schemper, M., & Harrell, F. E. (2011). Logistic regression modeling and the number of events per variable: selection bias dominates. Journal of Clinical Epidemiology, 64, 1464–65; author reply 1463–64.CrossRefGoogle ScholarPubMed
Vittinghoff, E., & McCulloch, C. E. (2007). Relaxing the rule of ten events per variable in logistic and Cox regression. American Journal of Epidemiology, 165, 710–8.Google Scholar
Wolter, K. M. (1985). Introduction to Variance Estimation. New York, NY: Springer-Verlag.Google Scholar
World Health Organization (2015). Global Status Report on Road Safety 2015. Geneva, Switzerland: WHO.Google Scholar
Wu, K. K., Li, F. W., & Cho, V. W. (2014). A randomized controlled trial of the effectiveness of brief-CBT for patients with symptoms of posttraumatic stress following a motor vehicle crash. Behavioural and Cognitive Psychotherapy, 42, 3147.CrossRefGoogle ScholarPubMed
Wynants, L., Bouwmeester, W., Moons, K. G., et al. (2015). A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data. Journal of Clinical Epidemiology, 68, 1406–14.CrossRefGoogle ScholarPubMed
Zoellner, L. A., Foa, E. B., Brigidi, B. D., & Przeworski, A. (2000). Are trauma victims susceptible to “false memories”? Journal of Abnormal Psychology, 109, 517–24.CrossRefGoogle ScholarPubMed
Zou, K. H., O'Malley, A. J., & Mauri, L. (2007). Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation, 115, 654–7.CrossRefGoogle ScholarPubMed

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