Search

Objectives. We used cluster analysis to subdivide a population of injection drug users and identify previously unknown behavioral heterogeneity within that population. Methods. We applied cluster analysis techniques to data collected in a cross-sectional survey of injection drug users in Winnipeg, Manitoba. The clustering variables we used were based on receptive syringe sharing, ethnicity, and types of drugs injected. Results. Seven clusters were identified for both mate and female injection drug users. Some relationships previously revealed in our study setting, such as the known relationship between Talwin (pentazocine) and Ritalin (methylphenidate) use, injection in hotels, and hepatitis C virus prevalence, were confirmed through our cluster analysis approach. Also, relationships between drug use and infection risk not previously observed in our study setting were identified, an example being a cluster of female crystal methamphetamine users who exhibited high-risk behaviors but an absence or low prevalence of blood-borne pathogens. Conclusions. Cluster analysis was useful in both confirming relationships previously identified and identifying new ones relevant to public health research and interventions.

Abstract Background Solvent abuse is a particularly serious issue affecting Aboriginal people. Here we examine the association between solvent use and socio-demographic variables, drug-related risk factors, and pathogen prevalence in Aboriginal injection drug users (IDU) in Manitoba, Canada. Methods Data originated from a cross-sectional survey of IDU from December 2003 to September 2004. Associations between solvent use and variables of interest were assessed by multiple logistic regression. Results A total of 266 Aboriginal IDU were included in the analysis of which 44 self-reported recent solvent use. Hepatitis C infection was 81% in solvent-users, compared to 55% in those reporting no solvent use. In multivariable models, solvent-users were younger and more likely to be infected with hepatitis C (AOR: 3.5; 95%CI: 1.3,14.7), to have shared needles in the last six months (AOR: 2.6; 95%CI:1.0,6.8), and to have injected talwin & Ritalin (AOR: 10.0; 95%CI: 3.8,26.3). Interpretation High hepatitis C prevalence, even after controlling for risky injection practices, suggests that solvent users may form closed networks of higher risk even amongst an already high-risk IDU population. Understanding the social-epidemiological context of initiation and maintenance of solvent use is necessary to address the inherent inequalities encountered by this subpopulation of substance users, and may inform prevention strategies for other marginalized populations.

Abstract Background Previous studies have used social network variables to improve our understanding of HIV transmission. Similar analytic approaches have not been undertaken for hepatitis C (HCV) or B (HBV), nor used to conduct comparative studies on these pathogens within a single setting. Methods A cross-sectional survey consisting of a questionnaire and blood sample was conducted on injection drug users in Winnipeg between December 2003 and September 2004. Logistic regression analyses were used to correlate respondent and personal network data with HCV, HBV and HIV prevalence. Results At the multivariate level, pathogen prevalence was correlated with both respondent and IDU risk network variables. Pathogen transmission was associated with several distinct types of high-risk networks formed around specific venues (shooting galleries, hotels) or within users who are linked by their drug use preferences. Smaller, isolated pockets of IDUs also appear to exist within the larger population where behavioural patterns pose a lesser risk, unless or until, a given pathogen enters those networks. Conclusion The findings suggest that consideration of both respondent and personal network variables can assist in understanding the transmission patterns of HCV, HBV, and HIV. It is important to assess these effects for multiple pathogens within one setting as the associations identified and the direction of those associations can differ between pathogens.

Background: Respondent-driven sampling (RDS) is a successful data collection method used in hard-to-reach populations, like those experiencing or at high risk of drug dependence. Since its introduction in 1997, identifying appropriate methods for estimating population means and sampling variances has been challenging and numerous approaches have been developed for making inferences about these quantities. To guide researchers and practitioners in deciding which approach to use, this article reviews the literature on these methodological developments.Methods: A systematic review using four electronic databases was conducted in order to summarize the progress of RDS inference over the last 20 years and to provide insight to researchers on using the appropriate estimators in analyzing RDS data. Two independent reviewers selected the relevant abstracts and articles; thirty-two studies were included. The content of the studies was further categorized into developing and evaluating RDS mean and variance estimators.Results: The population mean estimator RDSIEGO and the sampling variance estimators associated with tree boot strapping were identified as promising methods as the most robust population mean and variance estimate, respectively; as these estimators rely on a fewer assumptions.Conclusions: RDS holds substantial promise as a sampling method for understanding populations at high risk. The varied approaches to inference with RDS data each rely on different assumptions, but some require fewer assumptions than others and provide more robust and accurate inferences, when their corresponding assumptions are met. [ABSTRACT FROM AUTHOR]

Objective: To identify and compare the drug-injecting network characteristics of cocaine and heroin injectors associated with a risk of HIV and hepatitis C virus (HCV)., Methods: Active injectors were recruited from syringe exchange and methadone programs. Characteristics of all participants and their social networks were elicited. Regression analysis using generalized estimating equations examined the network characteristics of injection drug users (IDUs) relative to cocaine or heroin use in the past 6 months., Results: Of 282 IDUs, 228 (81%) used cocaine and 54 (19%) used heroin as their primary injected drug. In analyses adjusted for age and gender, cocaine injectors compared with heroin injectors were more likely to live in unstable housing (odds ratio [OR] = 3.55, 95% confidence interval [CI]: 1.49 to 8.40), self-report HCV infection (OR = 4.69, 95% CI: 2.14 to 10.31), and have a greater number of IDUs in their social network (OR = 1.61, 95% CI: 1.14 to 2.28) and were less likely to be polydrug users (OR = 0.06, 95% CI: 0.02 to 0.16) and to have social support (OR = 0.97, 95% CI: 0.95 to 0.99). The injecting networks of cocaine users were more likely to have members who were older (OR = 1.08, 95% CI: 1.04 to 1.12), had a history of shooting gallery use (OR = 2.27, 95% CI: 1.08 to 4.76), and had shorter relationships with the subject (OR = 0.91, 95% CI: 0.85 to 0.97)., Conclusions: Beyond personal behaviors, HIV and HCV infection risk seems to be linked to social network traits that are determined by drug type. Prevention efforts to control the spread of bloodborne viruses among IDUs could benefit from tailoring interventions according to the type of drug used.