BACKGROUND: The objective of the current study was to examine predictors and moderators of response to two HIV sexual risk interventions of different content and duration for individuals in substance abuse treatment programs. METHODS: Participants were recruited from community drug treatment programs participating in the National Institute on Drug Abuse Clinical Trials Network (CTN). Data were pooled from two parallel randomized controlled CTN studies (one with men and one with women) each examining the impact of a multi-session motivational and skills training program, in comparison to a single-session HIV education intervention, on the degree of reduction in unprotected sex from baseline to 3- and 6- month follow-ups. The findings were analyzed using a zero-inflated negative binomial (ZINB) model. RESULTS: Severity of drug use (p < .01), gender (p < .001), and age (p < .001) were significant main effect predictors of number of unprotected sexual occasions (USOs) at follow-up in the non-zero portion of the ZINB model (men, younger participants, and those with greater severity of drug/alcohol abuse have more USOs). Monogamous relationship status (p < .001) and race/ethnicity (p < .001) were significant predictors of having at least one USO vs. none (monogamous individuals and African Americans were more likely to have at least one USO). Significant moderators of intervention effectiveness included recent sex under the influence of drugs/alcohol (p < .01 in non-zero portion of model), duration of abuse of primary drug (p < .05 in non-zero portion of model), and Hispanic ethnicity (p < .01 in the zero portion, p < .05 in the non-zero portion of model). CONCLUSION: These predictor and moderator findings point to ways in which patients may be selected for the different HIV sexual risk reduction interventions and suggest potential avenues for further development of the interventions for increasing their effectiveness within certain subgroups.
We have not found any resources mentioned in this publication.
SciCrunch is a data sharing and display platform. Anyone can create a custom portal where they can select searchable subsets of hundreds of data sources, brand their web pages and create their community. SciCrunch will push data updates automatically to all portals on a weekly basis. User communities can also add their own data to SciCrunch, however this is not currently a free service.