Literature DB >> 21530105

Understanding subtypes of inner-city drug users with a latent class approach.

S J Kuramoto1, A S B Bohnert, C A Latkin.   

Abstract

AIMS: We empirically identified subtypes of inner-city users of heroin and cocaine based on type of drug used and route of administration.
METHOD: The sample was recruited from the communities in Baltimore, MD (SHIELD study) and consisted of 1061 participants who used heroin and or cocaine in the past 6 months on a weekly basis or more. Latent class analysis (LCA) was used to identify subtypes of drug users based on type of drug and route of administration. Logistic regression was used to compare the subtypes on depressive symptoms, injection risk and drug network compositions.
FINDINGS: Inner-city drug users were classified into five subtypes: three subtypes of injection drug users (IDUs) [heroin injecting (n=134; 13%), polydrug and polyroute (n=88, 8%), and heroin and cocaine injecting (n=404, 38%)], and two subtypes with low proportions of IDUs (LIDUs) [heroin snorting (n=275, 26%) and crack smoking (n=160; 14%)]. The polydrug and polyroute subtype had the highest depressive symptoms risk among all subtypes. Injection risk was lowest in the heroin injecting subtype and significantly differed from heroin and cocaine injecting subtype. The IDU subtypes also varied in the drug network compositions. The LIDU subtypes had similar depressive symptoms risk but vastly differed in the drug network compositions.
CONCLUSIONS: Subgroups of inner-city cocaine and heroin users based on type and route of administration differed in their depressive symptoms, injection risk and drug network compositions. Future studies should longitudinally examine factors associated with transitioning across these subtypes to better inform prevention and treatment efforts.
Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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Mesh:

Year:  2011        PMID: 21530105      PMCID: PMC3153580          DOI: 10.1016/j.drugalcdep.2011.03.030

Source DB:  PubMed          Journal:  Drug Alcohol Depend        ISSN: 0376-8716            Impact factor:   4.492


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