Modeling Victoria's Injection Drug Users




Stone, Ryan Alexander

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The objective of this thesis is to examine random effect models applied to binary data. I will use classical and Bayesian inference to fit generalized linear mixed models to a specific data set. The data analyzed in this thesis comes from a study examining the injection practices of needle exchange clientele in Victoria, B.C. focusing on their risk networks. First, I will examine the application of social network analysis to the study of injection drug use, focusing on issues of gender, norms, and the problem of hidden populations. Next the focus will be on random effect models, where I will provide some background and a few examples pertaining to generalized linear mixed models (GLMMs). After GLMMs, I will discuss the nature of the injection drug use study and the data which will then be analyzed using a GLMM. Lastly, I will provide a discussion about my results of the GLMM analysis along with a summary of the injection practices of the needle exchange clientele.



Statistics, GLMM, GLM, Bayesian, Networks, Egocentric, Sharing, Harm reduction, Norms