Boeri Miriam, Whalen Thor, Tyndall Benjamin, Ballard Ellen
Kennesaw State University, Department of Sociology and Criminal Justice, Kennesaw GA, USA.
Subst Abuse Rehabil. 2011 May 1;2011(2):89-102. doi: 10.2147/SAR.S14871.
To better understand patterns of drug use trajectories over time, it is essential to have standard measures of change. Our goal here is to introduce measures we developed to quantify change in drug use behaviors. A secondary goal is to provide effective visualizations of these trajectories for applied use. We analyzed data from a sample of 92 older drug users (ages 45 to 65) to identify transition patterns in drug use trajectories across the life course. Data were collected for every year since birth using a mixed methods design. The community-drawn sample of active and former users were 40% female, 50% African American, and 60% reporting some college or greater. Their life histories provided retrospective longitudinal data on the diversity of paths taken throughout the life course and changes in drug use patterns that occurred over time. Bayesian analysis was used to model drug trajectories displayed by innovative computer graphics. The mathematical techniques and visualizations presented here provide the foundation for future models using Bayesian analysis. In this paper we introduce the concepts of transition counts, transition rates and relapse/remission rates, and we describe how these measures can help us better understand drug use trajectories. Depicted through these visual tools, measurements of discontinuous patterns provide a succinct view of individual drug use trajectories. The measures we use on drug use data will be further developed to incorporate contextual influences on the drug trajectory and build predictive models that inform rehabilitation efforts for drug users. Although the measures developed here were conceived to better examine drug use trajectories, the applications of these measures can be used with other longitudinal datasets.
为了更好地理解药物使用轨迹随时间的变化模式，拥有标准的变化衡量指标至关重要。我们的目标是介绍我们开发的用于量化药物使用行为变化的指标。第二个目标是为实际应用提供这些轨迹的有效可视化展示。我们分析了92名老年吸毒者（年龄在45至65岁之间）的样本数据，以确定整个生命历程中药物使用轨迹的转变模式。自出生以来每年都采用混合方法设计收集数据。社区抽取的现役和曾经吸毒者样本中，40%为女性，50%为非裔美国人，60%拥有大学及以上学历。他们的生活史提供了关于整个生命历程中所走路径的多样性以及随时间发生的药物使用模式变化的回顾性纵向数据。贝叶斯分析用于通过创新的计算机图形对药物轨迹进行建模。这里介绍的数学技术和可视化展示为未来使用贝叶斯分析的模型奠定了基础。在本文中，我们引入了转变计数、转变率和复发/缓解率的概念，并描述了这些指标如何帮助我们更好地理解药物使用轨迹。通过这些可视化工具描绘的不连续模式测量结果，提供了个体药物使用轨迹的简洁视图。我们用于药物使用数据的指标将进一步发展，以纳入对药物轨迹的背景影响，并建立可为吸毒者康复工作提供信息的预测模型。尽管这里开发的指标旨在更好地研究药物使用轨迹，但这些指标的应用也可用于其他纵向数据集。
