Drug use trajectory patterns among older drug users

Miriam Boeri, Thor Whalen, Benjamin Tyndall, Ellen BallardKennesaw State University, Department of Sociology and Criminal Justice, Kennesaw GA, USAAbstract: To better understand patterns of drug use trajectories over time, it is essential to have standard measures of change. Our goal here is to intr...

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Main Authors: Tyndall B, Whalen T, Boeri M, Ballard E
Format: Article
Language:English
Published: Dove Medical Press 2011-05-01
Series:Substance Abuse and Rehabilitation
Online Access:http://www.dovepress.com/drug-use-trajectory-patterns-among-older-drug-users-a7461
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author Tyndall B
Whalen T
Boeri M
Ballard E
author_facet Tyndall B
Whalen T
Boeri M
Ballard E
author_sort Tyndall B
collection DOAJ
description Miriam Boeri, Thor Whalen, Benjamin Tyndall, Ellen BallardKennesaw State University, Department of Sociology and Criminal Justice, Kennesaw GA, USAAbstract: 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.Keywords: drug use, trajectory patterns, mixed methods, older adults
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spelling doaj.art-6b62b59389614de39dc9a85b39f16b5f2022-12-22T02:58:12ZengDove Medical PressSubstance Abuse and Rehabilitation1179-84672011-05-012011default89102Drug use trajectory patterns among older drug usersTyndall BWhalen TBoeri MBallard EMiriam Boeri, Thor Whalen, Benjamin Tyndall, Ellen BallardKennesaw State University, Department of Sociology and Criminal Justice, Kennesaw GA, USAAbstract: 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.Keywords: drug use, trajectory patterns, mixed methods, older adultshttp://www.dovepress.com/drug-use-trajectory-patterns-among-older-drug-users-a7461
spellingShingle Tyndall B
Whalen T
Boeri M
Ballard E
Drug use trajectory patterns among older drug users
Substance Abuse and Rehabilitation
title Drug use trajectory patterns among older drug users
title_full Drug use trajectory patterns among older drug users
title_fullStr Drug use trajectory patterns among older drug users
title_full_unstemmed Drug use trajectory patterns among older drug users
title_short Drug use trajectory patterns among older drug users
title_sort drug use trajectory patterns among older drug users
url http://www.dovepress.com/drug-use-trajectory-patterns-among-older-drug-users-a7461
work_keys_str_mv AT tyndallb drugusetrajectorypatternsamongolderdrugusers
AT whalent drugusetrajectorypatternsamongolderdrugusers
AT boerim drugusetrajectorypatternsamongolderdrugusers
AT ballarde drugusetrajectorypatternsamongolderdrugusers