Prevention for HIV + persons is a top priority for the Centers for Disease Control and Prevention (1
). We previously demonstrated that when young people living with HIV (YPLH) attended a three-module intervention delivered in small groups (Teens Linked to Care [TLC]) (2
) or in individual sessions (Choosing Life, Empowerment, Action, and Results [CLEAR]) (4
), the YPLH reduced their unprotected sexual acts. Drug abuse was an entry criterion for this study. Therefore, all participants were active users at the time of recruitment. When using logistic regression analyses that examined only reductions in use (4
), we did not find significant reductions in drug use as a function of the intervention. The current analysis evaluates a different, more powerful analytic strategy that allows the simultaneous examination of both elimination of use of a specific drug (a yes-no event) and reductions in use over time. In this article we re-examine the effects of the intervention on drug abuse, considering the impact of the highly skewed distributions in the reports of drug use by young people.
YPLH use a broad range of drugs: e.g., marijuana, cocaine, methamphetamine, heroin, and ketamine (2
). In a typical sample of substance-using young people, most young people do not use each drug. Thus, if we analyze the impact of reducing drug use from use
, a very high proportion of participants will be non-users. demonstrates that there is zero-inflation in the reports of drug use at baseline among YPLH in the CLEAR intervention (5
). The usage distribution is very skewed.
Number of times using each substance over the past three months
Given these distributions, standard regression techniques are inadequate for analyzing the outcomes, in this case, the impact of an intervention case management approach on reducing drug abuse. When analyzing drug use, both binomial (use/non-use
) and count data are combined in these distributions. To more sensitively examine the impact of an intervention on drug use, we utilize longitudinal zero-inflated Poisson (ZIP) models (6
) to examine reductions of the frequency of use among drug users.