Literature DB >> 19283949

Applying classification and regression tree analysis to identify prisoners with high HIV risk behaviors.

Linda Frisman1, Michael Prendergast, Hsiu-Ju Lin, Eleni Rodis, Lisa Greenwell.   

Abstract

Among prisoners, past research has associated several factors with HIV risk behaviors, including illicit drug use, engaging in sex trade, older age (for drug-related risk), younger age (for sex-related risk), low education, low income, type of offense, history of abuse, mental health disorders, vulnerability and low self-perceived efficacy. This study employs data collected through the Transitional Case Management study of the Criminal Justice Drug Abuse Treatment Studies collaborative to analyze characteristics of prisoners who engaged in high-risk behaviors prior to incarceration. For the first 787 participants of this study, we employed recursive partitioning techniques to better identify groups at varying levels of HIV risk behaviors. Those more likely to engage in risky needle use were White and either unemployed and less likely to justify their behavior, or employed with poor decision making capacity. Risky sexual behavior was associated with a general tendency toward risk-taking or a history of unstable housing. Those engaging in any type of HIV risk behavior were risk-takers in general and were aged 25 to 47 with a history of unstable housing. Recursive partitioning, a technique seldom used previously, offers a useful method for identifying subpopulations at elevated risk for HIV risk behaviors.

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Year:  2008        PMID: 19283949      PMCID: PMC3759152          DOI: 10.1080/02791072.2008.10400651

Source DB:  PubMed          Journal:  J Psychoactive Drugs        ISSN: 0279-1072


  23 in total

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Review 2.  Classification and regression tree analysis in public health: methodological review and comparison with logistic regression.

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Review 4.  HIV in prison: what are the risks and what can be done?

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5.  HIV transmission among male inmates in a state prison system--Georgia, 1992-2005.

Authors: 
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6.  Prevalence and incidence of HIV among incarcerated and reincarcerated women in Rhode Island.

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7.  Assessing the relationship between child sexual abuse and marginal living conditions on HIV/AIDS-related risk behavior among women prisoners.

Authors:  J L Mullings; J W Marquart; V E Brewer
Journal:  Child Abuse Negl       Date:  2000-05

8.  Women inmates' risky sex and drug behaviors: are they related?

Authors:  N U Cotten-Oldenburg; B K Jordan; S L Martin; L Kupper
Journal:  Am J Drug Alcohol Abuse       Date:  1999-02       Impact factor: 3.829

9.  Development and validation of a Client Problem Profile and Index for drug treatment.

Authors:  George W Joe; D Dwayne Simpson; Jack M Greener; Grace A Rowan-Szal
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10.  Using clinical classification trees to identify individuals at risk of STDs during pregnancy.

Authors:  Trace S Kershaw; Jessica Lewis; Claire Westdahl; Yun F Wang; Sharon Schindler Rising; Zohar Massey; Jeannette Ickovics
Journal:  Perspect Sex Reprod Health       Date:  2007-09
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  7 in total

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3.  Relationship influence and health risk behavior among re-entering women offenders.

Authors:  Michele Staton-Tindall; Linda Frisman; Hsui-Ju Lin; Carl Leukefeld; Carrie Oser; Jennifer R Havens; Michael Prendergast; Hilary L Surratt; Jennifer Clarke
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4.  Using classification and regression tree analysis to explore parental influenza vaccine decisions.

Authors:  Yuki Lama; Gregory R Hancock; Vicki S Freimuth; Amelia M Jamison; Sandra Crouse Quinn
Journal:  Vaccine       Date:  2019-12-02       Impact factor: 3.641

5.  Identifying smoker subgroups with high versus low smoking cessation attempt probability: A decision tree analysis approach.

Authors:  Hua-Hie Yong; Chandan Karmakar; Ron Borland; Shitanshu Kusmakar; Matthew Fuller-Tyszkiewicz; John Yearwood
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Authors:  Samantha K Stephens; Elizabeth G Eakin; Bronwyn K Clark; Elisabeth A H Winkler; Neville Owen; Anthony D LaMontagne; Marj Moodie; Sheleigh P Lawler; David W Dunstan; Genevieve N Healy
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7.  The process and utility of classification and regression tree methodology in nursing research.

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  7 in total

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