Identifying Risk Profiles for Substance Use and Comorbid Behavior

描述（由研究者提供）：青少年和年轻人的物质使用和共病健康相关行为对社会有很高的代价。尽管在制定有效的预防干预方案方面取得了相当大的进展，但其有效性在很大程度上取决于对多重风险共存的清晰认识。世界各地的预防科学家都可以获得关于发育结果的早期和并发风险因素的丰富数据来源。许多这些数据集先前已被分析，目的是检查风险因素，事实上，在确定描述个体和多重风险如何导致药物滥用等疾病的发展模型方面取得了很大进展。然而，现在有可能超越以前使用的传统风险建模方法，以便在多个层面上检查风险因素之间的复杂相互作用。通过从传统的“风险因素”方法转向新的“风险概况”方法，干预科学家将获得对风险的强有力的新理解。本研究采用了一种相对较新的、未被充分利用的以人为中心的统计技术，即协变量潜类分析，以(a)在现有的经验数据中识别细微的多层次风险概况，(b)建立风险概况预测未来问题行为的能力。待分析的数据集来自两项社区和两项国家纵向研究，包含关于各种风险因素以及物质使用和共病行为的丰富数据。将探讨性别和族裔群体在风险概况流行率方面的差异，以及风险概况成员资格与后来与健康有关的结果之间的联系。这项拟议的研究将填补目前关于多种风险在多个层面上相互作用的知识的重要空白，从而帮助干预科学家制定更有效的计划，并更有效地针对这些计划。一系列的文章将被提交给同行评审期刊，一个项目网站将被添加到宾夕法尼亚州立大学方法论中心的网站上，在那里可以免费下载用于潜在类建模的SAS软件。该项目网站将为干预科学家提供有关如何使用潜在类分析来发现他们自己数据中的风险概况的信息。青少年和青壮年的物质使用和共病健康相关行为给社会带来了高昂的代价。尽管在制定有效的预防干预方案方面取得了相当大的进展，但其有效性在很大程度上取决于对多重风险共存的清晰认识。这项拟议的研究将填补目前关于多种风险在多个层面上相互作用的知识的重要空白，从而帮助干预科学家制定更有效的计划，并更有效地针对这些计划。

DESCRIPTION (provided by investigator): Substance use and comorbid health-related behaviors among youth and young adults have a high cost to society. Although considerable progress has been made in developing effective intervention programs for prevention, their effectiveness depends to a large extent on a clear understanding of multiple risks as they coexist. Prevention scientists everywhere have access to rich sources of data on early and concurrent risk factors for developmental outcomes. Many of these data sets have been previously analyzed with the objective of examining risk factors, and in fact much progress has been made in the identification of developmental models that describe how individual and multiple risks contribute to disorders such as substance abuse. However, it is now possible to move beyond the traditional approaches to modeling risk that have been used previously in order to examine the complex interplay among risk factors at multiple levels. Intervention scientists stand to gain a powerful new understanding of risk by moving from a traditional 'risk factors' approach to a novel 'risk profiles' approach. The proposed research employs a relatively new and underutilized person-centered statistical technique, latent class analysis with covariates, to (a) identify nuanced multilevel risk profiles in existing empirical data and (b) establish the ability of the risk profiles to predict future problem behavior. The data sets to be analyzed are from two community-based and two national longitudinal studies and contain rich data on a variety of risk factors as well as substance use and comorbid behaviors. Gender and ethnic group differences will be explored in the prevalence of risk profiles and the link between risk profile membership and later health-related outcomes. The proposed research will fill an important gap in current knowledge about the interplay of multiple risks at multiple levels, thereby helping intervention scientists to develop more effective programs and to target those programs more effectively. A series of articles will be submitted to peer-review journals, and a project Web site will be added to the Methodology Center Web site at Penn State, where free SAS software for latent class modeling currently is available for download. The project site will provide information for intervention scientists on how to use latent class analysis to uncover risk profiles in their own data. Substance use and comorbid health-related behaviors among youth and young adults have a high cost to society. Although considerable progress has been made in developing effective intervention programs for prevention, their effectiveness depends to a large extent on a clear understanding of multiple risks as they coexist. The proposed research will fill an important gap in current knowledge about the interplay of multiple risks at multiple levels, thereby helping intervention scientists to develop more effective programs and to target those programs more effectively.