Exploration of a Factor Mixture-Based Taxonic-Dimensional Model of Anxiety Sensitivity and Transdiagnostic Psychopathology Vulnerability Among Trauma-Exposed Adults

Abstract

The aim of this study was to evaluate the associations between a factor mixture-based taxonic-dimensional model of anxiety sensitivity (AS) and posttraumatic stress, panic, generalized anxiety, depression, psychiatric multimorbidity, and quality of life among a young adult sample exposed to traumatic stress (N = 103, n _females = 66, M _age = 23.68 years, SD _age = 9.55). Findings showed support for the conceptual and operational utility of the AS taxonic-dimensional model with respect to concurrent transdiagnostic vulnerability among trauma-exposed adults. Specifically, relative to the low-AS group, the high-AS group demonstrated elevated levels of panic, depressive, and posttraumatic stress symptom severity as well as greater psychiatric multimorbidity and poorer quality of life. Furthermore, past-month MDD, GAD, PTSD, and panic attacks occurred nearly exclusively among the high-AS group. Continuous AS physical and psychological concerns scores were found to be significantly related to levels of panic and posttraumatic stress symptom severity, psychiatric multimorbidity as well as panic attack status only among the high-AS group and not among the low-AS group. Findings are discussed with respect to their implications for the conceptual and operational utility of the FMM-based taxonic-dimensional model of AS, related vulnerability for psychopathology in the context of trauma, and the clinical implications of these findings for assessment and intervention.

Keywords:

• anxiety sensitivity
• traumatic stress
• transdiagnostic risk

Acknowledgements

Dr. Bernstein recognizes the funding support from the Israeli Council for Higher Education Yigal Alon Fellowship, the European Union FP-7 Marie Curie Fellowship International Reintegration Grant, the National Institutes of Health (NIDA) Clinical LRP, Psychology Beyond Borders Mission Award, Israel Science Foundation, and the Rothschild-Caesarea Foundation's Returning Scientists Project at the University of Haifa. Dr. Marshall-Berenz recognizes the support from the National Institute of Mental Health National Research Service Award. Mr. Zvielli recognizes the support from the President's Doctoral Fellowship at the University of Haifa.

Notes

^1. FMM may have a number of significant advantages relative to CCK taxometric methods as well as relative to other latent mixture modeling techniques (Bauer & Curran, 2004; Lubke & Tueller, 2010; Muthén, 2008). Unlike other more commonly employed data analytic strategies such as CCK taxometrics, K-means clustering, latent class and latent profile analyses, and factor analysis, FMM facilitates the concurrent modeling of various models that incorporate latent class (categorical) structure and within-class continuity. Thus, FMM is not limited to testing (1) whether a latent variable is either a dichotomous categorical (taxonic) or continuous (i.e., CCK taxometrics) variable, (2) the relative fit of various continuous models (i.e., factor analysis) assuming a single latent homogeneous population, or (3) the relative fit of various categorical models that lack within-class continuity because of assumption of local independence (i.e., K-means clustering, latent class/profile analysis). Rather, FMM permits more construct valid and flexible latent structural modeling of various possible categorical and continuous structures simultaneously. Furthermore, and unlike CCK taxometrics, FMM offers a model-based approach in which latent structural models are compared and contrasted in terms of multiple, well-established, objective fit indices. In addition, unlike CCK taxometrics, and similar to other model-based mixture techniques, FMM imposes no limit on the number of possible latent classes that may underlie a construct's putative population heterogeneity or latent class structure. Finally, because FMM incorporates mixture modeling and factor analytic techniques, it is rooted in extensive and well-established quantitative theory and methods. Relatedly, unlike CCK taxometrics in which procedures may be applied and their results interpreted in a variety of (unstandardized) ways, FMM is applied and interpreted in a highly standardized manner (e.g., Lubke & Muthén, 2005). Consequently, unlike CCK taxometric research, application and interpretation of FMM is more likely to yield reliable and valid latent structural findings that consequently may be more replicable across studies and between independent research groups.

^2. Generalized anxiety was only measured categorically in the present study—there was no measure in this study of continuous generalized anxiety symptom severity.

^3. Because of the observed low base rate of PTSD diagnostic status, we were not able to conduct the planned analysis involving PTSD as a categorical dependent variable, though, as in past research of traumatic stress and posttraumatic stress in community samples (Blanchard, Hickling, Taylor, Loos, & Gerardi, 1994; Shalev et al., 1998), analyses of continuous CAPS posttraumatic stress symptom severity levels were carried out. Furthermore, consistent with this approach, replicated taxometric study of PTSD (albeit limited methodologically as noted above) indicate that continuous measurement of the putative psychopathological syndrome, as opposed to categorical diagnostic status, provides a more valid means of measuring and studying posttraumatic stress (Forbes, Haslam, Williams, & Creamer, 2005; Ruscio, Ruscio, & Keane, 2002).