Identifying Risk Profiles for Substance Use and Comorbid Behavior

确定药物使用和共病行为的风险概况

• 批准号: 7502099
• 负责人: STEPHANIE T LANZA
• 金额: $ 6.22万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-30 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7502099
• 关键词: Alcohol or Other Drugs use; Basic Science; Behavior; Class; Communities; Complex; Computer software; Data; Data Set; Data Sources; Development; Disease; Distal; Effectiveness; Ethnic group; Family; Future; Gender; Goals; Individual; Information Dissemination; Internet; Intervention; Journals; Knowledge; Link; Longitudinal Studies; Methodology; Modeling; Neighborhoods; Outcome; Peer Review; Prevalence; Prevention; Prevention Research; Principal Investigator; Problem behavior; Purpose; Range; Regression Analysis; Research; Research Personnel; Risk; Risk Assessment; Risk Factors; SAS; Science; Scientist; Series; Sex Characteristics; Site; Societies; Solutions; Standards of Weights and Measures; Statistical Methods; Structure; Subgroup; Substance abuse problem; Techniques; Youth; base; cost; innovation; intervention program; knowledge base; novel; novel strategies; person centered; programs; prospective; young adult

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.

描述（调查员提供）：青年和年轻人的药物使用和与健康相关的行为对社会的成本很高。尽管在制定有效的预防干预计划方面取得了长足的进步，但它们的有效性在很大程度上取决于对多种风险的共存清晰了解。各地的预防科学家都可以获取有关早期和同时发生危险因素的丰富数据来源。以前已经对这些数据集进行了研究，以检查风险因素的目的，实际上在识别发展模型中已经取得了很大进展，这些发展模型描述了个体和多种风险如何促进诸如药物滥用之类的疾病。但是，现在可以超越传统的方法来建模风险，以检查多个级别的风险因素之间的复杂相互作用。干预科学家将通过从传统的“风险因素”方法转向新颖的“风险概况”方法来获得对风险的有力新理解。拟议的研究采用了一种相对较新的，以人为中心的统计技术，具有协变量的潜在类别分析，以（a）确定现有经验数据中的细微差别的多层次风险概况，（b）确定风险概况预测未来问题行为的能力。要分析的数据集来自两个基于社区的和两个国家纵向研究，并包含有关各种风险因素以及药物使用和合并行为的丰富数据。在风险概况的普遍性以及风险概况成员资格与后来与健康相关的结果之间的联系中，将探讨性别和种族差异。拟议的研究将填补有关当前有关多个级别多种风险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。一系列文章将提交给PEER-REVIEW期刊，并将将项目网站添加到Penn State的方法论中心网站上，该网站当前可供下载潜在的类型建模的免费SAS软件。该项目站点将为干预科学家提供有关如何使用潜在类别分析来揭示其数据中风险概况的信息。青年和年轻人的药物使用和与健康相关的行为对社会的成本很高。尽管在制定有效的预防干预计划方面取得了长足的进步，但它们的有效性在很大程度上取决于对多种风险的共存清晰了解。拟议的研究将填补有关当前有关多个级别多种风险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。