Classification models for subthreshold generalized anxiety disorder in a college population: Implications for prevention
Classification models for subthreshold generalized anxiety disorder in a college population: Implications for prevention
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DOI：
10.1016/j.janxdis.2015.05.011
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发表时间：
2015-08-01
影响因子：
10.3
通讯作者：
Newman, Michelle G.
中科院分区：
文献类型：
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作者：
Kanuri, Nitya;Taylor, C. Barr;Newman, Michelle G.
广泛性焦虑症（GAD）是大学校园中最常见的精神疾病之一，通常无法识别和治疗。我们提出了一个综合的预防和治疗模式，包括循证自助（SH）和指导自助（GSH）干预来解决这个问题。为了为这种干预提供分步护理模式的发展提供信息，我们评估了对大学生进行基于人群的焦虑筛查的结果。开发了一个初步模型，以说明如何将不断增加的疟疾学水平与预防/治疗干预措施联系起来。我们使用筛查数据提出了四种GAD风险人群的分类模型。然后，我们探讨了实施这种预防/治疗分步护理模式的成本考虑。在2489名大学生（平均年龄19.1岁; 67%为女性）中，8.0%（198/2489）符合DSM-5 GAD临床标准，与该人群的预期临床发生率一致。风险模型1（低于阈值，但有相当多的焦虑症状）确定13.7%的学生可能有患GAD的风险。模型2（亚阈值，但GAD症状严重程度高）确定了13.7%。模型3（低于阈值，但症状令人痛苦）确定为12.3%。模型4（低于阈值，但相当担心）识别率为17.4%。这些模型之间几乎没有重叠，合并的风险人群为39.4%。这些模式在确定真正处于风险中的人方面的效率以及预防性干预措施的成本和效力将决定预防是否可行。使用模型1的数据和保守的成本估计，我们发现，即使是0.2的预防干预效果大小也可以使预防/治疗模型比现有的“等待和治疗”模型更具成本效益。“（C）2015 Elsevier Ltd.保留所有权利。
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." (C) 2015 Elsevier Ltd. All rights reserved.