Abstract

We examined characteristics associated with HIV stigma and evaluated a multi-component video designed to normalize HIV and reduce HIV stigma. Three pharmacies located in heavy, drug-active neighborhoods in New York City and registered to sell nonprescription syringes were trained to recruit their nonprescription syringe customers who inject drugs and their under/uninsured customers. Syringe customer participants were trained to recruit up to three of their peers. As part of a larger intervention to increase HIV testing, participants in two of three study arms viewed the “Health Screenings for Life” video and were administered pre/post-video surveys capturing HIV stigma. Participants in the nonvideo arm were administered one assessment of HIV stigma. Log-binomial regression with generalized estimating equations to account for clustering of peer networks was used to: (1) determine factors associated with HIV stigma and (2) determine differences in HIV stigma by study arm. A total of 716 participants were recruited. Factor analyses showed HIV stigma measures loading on two factors: HIV blame and HIV shame. After adjustment, HIV blame was positively associated with younger age (PR: 1.24; 95% CI: 1.07–1.43) and inversely associated with educational attainment (PR: 0.66; 95% CI: 0.58–0.76) and employment (PR: 0.76; 95% CI: 0.60–0.96). HIV shame was inversely associated with educational attainment (PR: 0.75; 95% CI: 0.62–0.92), HIV-positive status (PR: 0.60; 95% CI: 0.39–0.92), and injecting drugs (PR: 0.72; 95% CI: 0.54–0.94) and was positively associated with multiple sex partnerships (PR: 1.24; 95% CI: 1.01–1.52). Those who viewed the video were also less likely to report HIV blame and HIV shame, post-video, compared to those in the nonvideo arm. These data provide evidence of an association between HIV stigma and lower socioeconomic status groups, and between HIV stigma and HIV sexual risk. These data also provide evidence that a multi-component video aimed at normalizing HIV may assist in reducing HIV stigma in heavy, drug-active neighborhoods.

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