Literature DB >> 25313832

Patterns of drug use, risky behavior, and health status among persons who inject drugs living in San Diego, California: a latent class analysis.

Alexis M Roth1, Richard A Armenta, Karla D Wagner, Scott C Roesch, Ricky N Bluthenthal, Jazmine Cuevas-Mota, Richard S Garfein.   

Abstract

BACKGROUND: Among persons who inject drugs (PWID), polydrug use (the practice of mixing multiple drugs/alcohol sequentially or simultaneously) increases risk for HIV transmission and unintentional overdose deaths. Research has shown local drug markets influence drug use practices. However, little is known about the impact of drug mixing in markets dominated by black tar heroin and methamphetamine, such as the western United States.
METHODS: Data were collected through an ongoing longitudinal study examining drug use, risk behavior, and health status among PWID. Latent class analysis (LCA) was used to identify patterns of substance use (heroin, methamphetamine, prescription drugs, alcohol, and marijuana) via multiple administration routes (injecting, smoking, and swallowing). Logistic regression was used to identify behaviors and health indicators associated with drug use class.
RESULTS: The sample included 511 mostly white (51.5%) males (73.8%), with mean age of 43.5 years. Two distinct classes of drug users predominated: methamphetamine by multiple routes (51%) and heroin by injection (49%). In multivariable logistic regression, class membership was associated with age, race, and housing status. PWID who were HIV-seropositive and reported prior sexually transmitted infections had increased odds of belonging to the methamphetamine class. Those who were HCV positive and reported previous opioid overdose had an increased odds of being in the primarily heroin injection class (all P-values < .05).
CONCLUSION: Risk behaviors and health outcomes differed between PWID who primarily inject heroin vs. those who use methamphetamine. The findings suggest that in a region where PWID mainly use black tar heroin or methamphetamine, interventions tailored to sub-populations of PWID could improve effectiveness.

Entities:  

Keywords:  California; San Diego; latent class analysis; persons who inject drugs; polydrug use; tailored interventions

Mesh:

Substances:

Year:  2014        PMID: 25313832      PMCID: PMC4356115          DOI: 10.3109/10826084.2014.962661

Source DB:  PubMed          Journal:  Subst Use Misuse        ISSN: 1082-6084            Impact factor:   2.164


  29 in total

1.  Explaining the geographical variation of HIV among injection drug users in the United States.

Authors:  D Ciccarone; P Bourgois
Journal:  Subst Use Misuse       Date:  2003-12       Impact factor: 2.164

2.  Heroin overdose among young injection drug users in San Francisco.

Authors:  Kristen C Ochoa; Peter J Davidson; Jennifer L Evans; Judith A Hahn; Kimberly Page-Shafer; Andrew R Moss
Journal:  Drug Alcohol Depend       Date:  2005-06-14       Impact factor: 4.492

3.  Inactivation and survival of hepatitis C virus on inanimate surfaces.

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4.  Drug sharing among heroin networks: implications for HIV and hepatitis B and C prevention.

Authors:  Stephen Koester; Jason Glanz; Anna Barón
Journal:  AIDS Behav       Date:  2005-03

5.  Patterns of drug use and abuse among aging adults with and without HIV: a latent class analysis of a US Veteran cohort.

Authors:  Traci C Green; Trace Kershaw; Haiqun Lin; Robert Heimer; Joseph L Goulet; Kevin L Kraemer; Adam J Gordon; Steve A Maisto; Nancy L Day; Kendall Bryant; David A Fiellin; Amy C Justice
Journal:  Drug Alcohol Depend       Date:  2010-04-15       Impact factor: 4.492

6.  HCV infection prevalence lower than expected among 18-40-year-old injection drug users in San Diego, CA.

Authors:  Richard S Garfein; Amanda Rondinelli; Richard F W Barnes; Jazmine Cuevas; Mitcheal Metzner; Michele Velasquez; David Rodriguez; Meredith Reilly; Jian Xing; Eyasu H Teshale
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7.  Use of item response theory and latent class analysis to link poly-substance use disorders with addiction severity, HIV risk, and quality of life among opioid-dependent patients in the Clinical Trials Network.

Authors:  Li-Tzy Wu; Walter Ling; Bruce Burchett; Dan G Blazer; Chongming Yang; Jeng-Jong Pan; Bryce B Reeve; George E Woody
Journal:  Drug Alcohol Depend       Date:  2011-04-17       Impact factor: 4.492

8.  Comparison of HIV infection risk behaviors among injection drug users from East and West Coast US cities.

Authors:  Richard S Garfein; Edgar R Monterroso; Tony C Tong; David Vlahov; Don C Des Jarlais; Peter Selwyn; Peter R Kerndt; Carl Word; M Daniel Fernando; Lawrence J Ouellet; Scott D Holmberg
Journal:  J Urban Health       Date:  2004-06       Impact factor: 3.671

9.  Heroin in brown, black and white: structural factors and medical consequences in the US heroin market.

Authors:  Daniel Ciccarone
Journal:  Int J Drug Policy       Date:  2008-10-21

Review 10.  An evolving problem: methamphetamine production and trafficking in the United States.

Authors:  Rashi K Shukla; Jordan L Crump; Emelia S Chrisco
Journal:  Int J Drug Policy       Date:  2012-09-01
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  21 in total

1.  Characterizing profiles of polysubstance use among high school students in Baltimore, Maryland: A latent class analysis.

Authors:  Kristin E Schneider; Sherri-Chanelle Brighthaupt; Abigail K Winiker; Renee M Johnson; Rashelle J Musci; Sabriya L Linton
Journal:  Drug Alcohol Depend       Date:  2020-04-25       Impact factor: 4.492

2.  Drug use and phylogenetic clustering of hepatitis C virus infection among people who use drugs in Vancouver, Canada: A latent class analysis approach.

Authors:  B Jacka; B C Bray; T L Applegate; B D L Marshall; V D Lima; K Hayashi; K DeBeck; J Raghwani; P R Harrigan; M Krajden; J S G Montaner; J Grebely
Journal:  J Viral Hepat       Date:  2017-09-04       Impact factor: 3.728

3.  A Hazard Analysis of Risk Factors of Mortality in Individuals Who Inject Drugs in Denver CO.

Authors:  Jonathan M Davis; Katie Suleta; Karen F Corsi; Robert E Booth
Journal:  AIDS Behav       Date:  2017-04

4.  Prevalence and Correlates of Heroin-Methamphetamine Co-Injection Among Persons Who Inject Drugs in San Diego, California, and Tijuana, Baja California, Mexico.

Authors:  Meredith C Meacham; Steffanie A Strathdee; Gudelia Rangel; Richard F Armenta; Tommi L Gaines; Richard S Garfein
Journal:  J Stud Alcohol Drugs       Date:  2016-09       Impact factor: 2.582

5.  Latent classes of polydrug and polyroute use and associations with human immunodeficiency virus risk behaviours and overdose among people who inject drugs in Tijuana, Baja California, Mexico.

Authors:  Meredith C Meacham; Scott C Roesch; Steffanie A Strathdee; Suzanne Lindsay; Patricia Gonzalez-Zuniga; Tommi L Gaines
Journal:  Drug Alcohol Rev       Date:  2017-03-24

6.  A fragmented code: The moral and structural context for providing assistance with injection drug use initiation in San Diego, USA.

Authors:  Andy Guise; Jason Melo; Maria Luisa Mittal; Claudia Rafful; Jazmine Cuevas-Mota; Peter Davidson; Richard S Garfein; Dan Werb
Journal:  Int J Drug Policy       Date:  2018-03-07

7.  Rural and urban comparisons of polysubstance use profiles and associated injection behaviors among people who inject drugs in Puerto Rico.

Authors:  Dane Hautala; Roberto Abadie; Bilal Khan; Kirk Dombrowski
Journal:  Drug Alcohol Depend       Date:  2017-10-18       Impact factor: 4.492

8.  Heroin and Methamphetamine Injection: An Emerging Drug Use Pattern.

Authors:  Alia Al-Tayyib; Stephen Koester; Sig Langegger; Lisa Raville
Journal:  Subst Use Misuse       Date:  2017-03-21       Impact factor: 2.164

9.  A latent class analysis of alcohol and drug use immediately before or during sex among women.

Authors:  Grace L Reynolds; Dennis G Fisher
Journal:  Am J Drug Alcohol Abuse       Date:  2018-10-25       Impact factor: 3.829

10.  Factors associated with patterns of mobile technology use among persons who inject drugs.

Authors:  Kelly M Collins; Richard F Armenta; Jazmine Cuevas-Mota; Lin Liu; Steffanie A Strathdee; Richard S Garfein
Journal:  Subst Abus       Date:  2016-04-19       Impact factor: 3.716

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