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J Anxiety Disord. 2015 Aug;34:43-52. doi: 10.1016/j.janxdis.2015.05.011. Epub 2015 Jun 17.

Classification models for subthreshold generalized anxiety disorder in a college population: Implications for prevention.

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Stanford University School of Medicine, Department of Psychiatry and Behavioral Sciences, United States. Electronic address:
Stanford University School of Medicine, Department of Psychiatry and Behavioral Sciences, United States; Palo Alto University, United States.
PGSP-Stanford PsyD Consortium, United States.
The Pennsylvania State University, Department of Psychology, United States.


Generalized anxiety disorder (GAD) is one of the most common psychiatric disorders on college campuses and often goes unidentified and untreated. We propose a combined prevention and treatment model composed of evidence-based self-help (SH) and guided self-help (GSH) interventions to address this issue. To inform the development of this stepped-care model of intervention delivery, we evaluated results from a population-based anxiety screening of college students. A primary model was developed to illustrate how increasing levels of symptomatology could be linked to prevention/treatment interventions. We used screening data to propose four models of classification for populations at risk for GAD. We then explored the cost considerations of implementing this prevention/treatment stepped-care model. Among 2489 college students (mean age 19.1 years; 67% female), 8.0% (198/2489) met DSM-5 clinical criteria for GAD, in line with expected clinical rates for this population. At-risk Model 1 (subthreshold, but considerable symptoms of anxiety) identified 13.7% of students as potentially at risk for developing GAD. Model 2 (subthreshold, but high GAD symptom severity) identified 13.7%. Model 3 (subthreshold, but symptoms were distressing) identified 12.3%. Model 4 (subthreshold, but considerable worry) identified 17.4%. There was little overlap among these models, with a combined at-risk population of 39.4%. The efficiency of these models in identifying those truly at risk and the cost and efficacy of preventive interventions will determine if prevention is viable. Using Model 1 data and conservative cost estimates, we found that a preventive intervention effect size of even 0.2 could make a prevention/treatment model more cost-effective than existing models of "wait-and-treat."


College health; Generalized anxiety disorder; Prevention; Screening; Self-help interventions; Stepped-care models; e-Health

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