Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-22T18:22:16.116Z Has data issue: false hasContentIssue false

Diagnostic utility and factor structure of the PTSD Checklist in older adults

Published online by Cambridge University Press:  30 May 2012

Robert H. Pietrzak*
Affiliation:
National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, Connecticut, USA Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA National Center for Disaster Mental Health Research, White River Junction, Vermont, USA
Peter H. Van Ness
Affiliation:
Section of Geriatrics, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
Terri R. Fried
Affiliation:
Section of Geriatrics, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
Sandro Galea
Affiliation:
National Center for Disaster Mental Health Research, White River Junction, Vermont, USA Department of Epidemiology, Columbia University School of Public Health, New York, USA
Fran Norris
Affiliation:
National Center for Disaster Mental Health Research, White River Junction, Vermont, USA National Center for Posttraumatic Stress Disorder, White River Junction VA Medical Center, White River Junction, Vermont, USA Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
*
Correspondence should be addressed to: Robert H. Pietrzak, PhD, MPH, National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, Yale University School of Medicine, 950 Campbell Avenue 151E, West Haven, CT 06516, USA. Phone: +860-638-7467; Fax: +203-937-3481. Email: [email protected].
Get access

Abstract

Background: Little research has examined the diagnostic utility and factor structure of commonly used posttraumatic stress disorder (PTSD) assessment instruments in older persons.

Methods: A total of 206 adults aged 60 or older (mean age = 69 years; range = 60–92), who resided in the Galveston Bay area when Hurricane Ike struck in September 2008, completed a computer-assisted telephone interview two–five months after this disaster. Using the PTSD Checklist (PCL), PTSD symptoms were assessed related both to this disaster and to participants’ worst lifetime traumatic event. Total PCL scores were compared to PCL-based, Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV)-derived probable diagnoses of PTSD to determine optimal cut scores. Confirmatory factor analyses (CFAs) were conducted to evaluate PTSD symptom structure.

Results: Receiver operating characteristic analyses indicated that a PCL score of 39 achieved optimal sensitivity and specificity in assessing a PCL-based, algorithm-derived DSM-IV diagnosis of worst event-related PTSD; and that a score of 37 optimally assessed probable Ike-related PTSD. CFAs revealed that a recently proposed five-factor model – comprised of re-experiencing, avoidance, numbing, dysphoric arousal, and anxious arousal factors – provided a better fitting representation of both worst event- and disaster-related PTSD symptoms than alternative models. Current Ike-related anxious arousal symptoms demonstrated a significantly stronger association with current generalized anxiety than depressive symptoms, thereby supporting the construct validity of this five-factor model of PTSD symptomatology.

Conclusions: A PCL score of 37 to 39 may help identify probable PTSD in older persons. The expression of PTSD symptoms in older adults may be best characterized by a recently proposed five-factor model with distinct dysphoric arousal and anxious arousal clusters.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

American Psychiatric Association (2000). Diagnostic and Statistical Manual of Mental Disorders, 4th edn. Washington, DC: American Psychiatric Press.Google Scholar
Armour, C., Elhai, J. D., Richardson, D., Ractliffe, K., Wang, L. and Elklit, A. (2012). Assessing a five factor model of PTSD: is dysphoric arousal a unique PTSD construct showing differential relationships with anxiety and depression? Journal of Anxiety Disorders, 26, 368376.CrossRefGoogle ScholarPubMed
Asmundson, G. J., Stapleton, J. A. and Taylor, S. (2004). Are avoidance and numbing distinct PTSD symptom clusters? Journal of Traumatic Stress, 17, 467475.CrossRefGoogle ScholarPubMed
Averill, P. M. and Beck, J. G. (2000). Posttraumatic stress disorder in older adults: a conceptual review. Journal of Anxiety Disorders, 14, 133156.Google Scholar
Blanchard, E. B., Hickling, E. J., Taylor, A. E., Forneris, C. A., Loos, W. R. and Jaccard, J. (1995). Effects of varying scoring rules of the Clinician-Administered PTSD Scale (CAPS) for the diagnosis of post-traumatic stress disorder in motor vehicle accident victims. Behaviour Research and Therapy, 33, 471475.CrossRefGoogle ScholarPubMed
Breslau, N., Kessler, R., Chilcoat, H., Schultz, L., Davis, G. and Andreski, P. (1998). Trauma and posttraumatic stress disorder in the community: the 1996 Detroit area survey of trauma. Archives of General Psychiatry, 55, 626632.Google Scholar
Cook, J. M. and O'Donnell, C. (2005). Assessment and psychological treatment of posttraumatic stress disorder in older adults. Journal of Geriatric Psychiatry and Neurology, 18, 6171.Google Scholar
Cook, J. M., Elhai, J. D. and Areán, P. A. (2005). Psychometric properties of the PTSD checklist with older primary care patients. Journal of Traumatic Stress, 18, 371376.CrossRefGoogle ScholarPubMed
Elhai, J. D. and Palmieri, P. A. (2011). The factor structure of posttraumatic stress disorder: a literature update, critique of methodology, and agenda for future research. Journal of Anxiety Disorders, 25, 849854.CrossRefGoogle ScholarPubMed
Elhai, J. D., Biehn, T. L., Armour, C., Klopper, J. J., Frueh, B. C. and Palmieri, P. A. (2011). Evidence for a unique PTSD construct represented by PTSD's D1-D3 symptoms. Journal of Anxiety Disorders, 25, 340345.Google Scholar
Falk, B., Hersen, M. and Van Hasselt, V. (1994). Assessment of posttraumatic stress disorder in older adults: a critical review. Clinical Psychology Review, 14, 383415.Google Scholar
Fan, X. and Sivo, S. A. (2009). Using goodness-of-fit indexes in assessing mean structure invariance. Structural Equation Modeling, 16, 5467.Google Scholar
Galea, S., Nandi, A. and Vlahov, D. (2005). The epidemiology of post-traumatic stress disorder after disasters. Epidemiologic Reviews, 27, 7891.Google Scholar
Goenjian, A. K. et al. (1994). Posttraumatic stress disorder in elderly and younger adults after the 1988 earthquake in Armenia. American Journal of Psychiatry, 151, 895901.Google Scholar
Guggenmoos-Holzmann, I. (1993). How reliable are chance-corrected measures of agreement? Statistics in Medicine, 12, 21912205.CrossRefGoogle ScholarPubMed
Hu, L. and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 155.Google Scholar
Hudson, S. A., Beckford, L. A., Jackson, S. D. and Philpot, M. P. (2008). Validation of a screening instrument for post-traumatic stress disorder in a clinical sample of older adults. Aging and Mental Health, 12, 670673.CrossRefGoogle Scholar
Kraemer, H. C. (1992). Evaluating Medical Tests. Newbury Park, CA: Sage.Google Scholar
Kroenke, K., Spitzer, R. L. and Williams, J. B. (2001). The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine, 16, 606613.CrossRefGoogle ScholarPubMed
MacCallum, R. C., Browne, M. W. and Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130149.Google Scholar
McDonald, S. D. and Calhoun, P. S. (2010). The diagnostic accuracy of the PTSD checklist: a critical review. Clinical Psychology Review, 30, 976987.Google Scholar
Norris, F. H., Friedman, M. J., Watson, P. J., Byrne, C. M., Diaz, E. and Kaniasty, K. (2002). 60,000 disaster victims speak – Part I: an empirical review of the empirical literature, 1981–2001. Psychiatry, 65, 207239.CrossRefGoogle Scholar
Norris, F. H., Sherrieb, K. and Galea, S. (2010). Prevalence and consequences of disaster-related illness and injury from Hurricane Ike. Rehabilitation Psychology, 55, 221230.Google Scholar
Pietrzak, R. H., Tsai, J., Harpaz-Rotem, I., Whealin, J. M. and Southwick, S. M. (2012). Support for a novel 5-factor model of posttraumatic stress symptoms in three independent samples of veterans of the Iraq and Afghanistan wars: a confirmatory factor analytic study. Journal of Psychiatric Research, 46, 317322.CrossRefGoogle ScholarPubMed
Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111163.CrossRefGoogle Scholar
Raghunathan, T. E., Lepkowski, J. M., Van Hoewyk, J. and Solenberger, P. (2001). A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, 27, 8595.Google Scholar
Raghunathan, T. E., Solenberger, P. W. and Van Hoewyk, J. (2002). IVEware: Imputation and Variance Estimation Software – User Guide. Ann Arbor, MI: University of Michigan.Google Scholar
Satorra, A. and Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507514.Google Scholar
Schinka, J. A., Brown, L. M., Borenstein, A. R. and Mortimer, J. A. (2007). Confirmatory factor analysis of the PTSD checklist in the elderly. Journal of Traumatic Stress, 20, 281289.Google Scholar
Spitzer, R. L., Kroenke, K., Williams, J. B. and Lowe, B. (2006). A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of Internal Medicine, 166, 10921097.Google Scholar
Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87, 245251.Google Scholar
Tracy, M., Norris, F. H. and Galea, S. (2011). Differences in the determinants of posttraumatic stress disorder and depression after a mass traumatic event. Depression and Anxiety, 28, 666675.Google Scholar
Uddin, M. et al. (2010). Epigenetic and immune function profiles associated with posttraumatic stress disorder. Proceedings of the National Academy of Sciences, 107, 94709475.CrossRefGoogle ScholarPubMed
Uebersax, J. S. (1987). Diversity of decision-making models and the measurement of interrater agreement. Psychological Bulletin, 101, 140146.Google Scholar
U.S. Census Bureau (2008). American Community Survey: 2005–2007 Estimates. Available at: http://www.census.gov/acs/www/data_documentation/data_main/.Google Scholar
Wang, L. et al. (2011). Comparing alternative factor models of PTSD symptoms across earthquake victims and violent riot witnesses in China: evidence for a five-factor model proposed by Elhai et al. (2011). Journal of Anxiety Disorders, 25, 771776.Google Scholar
Watson, D. (2005). Rethinking the mood and anxiety disorders: a quantitative hierarchical model for DSM-V. Journal of Abnormal Psychology, 114, 522536.CrossRefGoogle ScholarPubMed
Weathers, F., Litz, B., Herman, D., Huska, J. and Keane, T. (1993). The PTSD Checklist (PCL): Reliability, Validity, and Diagnostic Utility. San Antonio, TX: Meeting of the International Society of Traumatic Stress Studies.Google Scholar
Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3, 3235.Google Scholar
Yufik, T. and Simms, L. J. (2010). A meta-analytic investigation of the structure of posttraumatic stress disorder symptoms. Journal of Abnormal Psychology, 119, 764776.Google Scholar