PURPOSE: To identify individual- and neighborhood-level correlates of membership within high HIV prevalence drug networks. METHODS: We recruited 378 New York City drug users via respondent-driven sampling (2006-2009). Individual-level characteristics and recruiter-recruit relationships were ascertained and merged with 2000 tract-level U.S. Census data. Descriptive statistics and population average models were used to identify correlates of membership in high HIV prevalence drug networks (>10.54% vs. <10.54% HIV). RESULTS: Individuals in high HIV prevalence drug networks were more likely to be recruited in neighborhoods with greater inequality (adjusted odds ratio [AOR], 5.85; 95% confidence interval [CI], 1.40-24.42), higher valued owner-occupied housing (AOR, 1.48; 95% CI, 1.14-1.92), and a higher proportion of Latinos (AOR, 1.83; 95% CI, 1.19-2.80). They reported more crack use (AOR, 7.23; 95% CI, 2.43-21.55), exchange sex (AOR, 1.82; 95% CI, 1.03-3.23), and recent drug treatment enrollment (AOR, 1.62; 95% CI, 1.05-2.50) and were less likely to report cocaine use (AOR, 0.40; 95% CI, 0.20-0.79) and recent homelessness (AOR, 0.32; 95% CI, 0.17-0.57). CONCLUSIONS: The relationship between exchange sex, crack use, and membership within high HIV prevalence drug networks may suggest an ideal HIV risk target population for intervention. Coupling network-based interventions with those adding risk-reduction and HIV testing/care/adherence counseling services to the standard of care in drug treatment programs should be explored in neighborhoods with increased inequality, higher valued owner-occupied housing, and a greater proportion of Latinos.
PURPOSE: To identify individual- and neighborhood-level correlates of membership within high HIV prevalence drug networks. METHODS: We recruited 378 New York City drug users via respondent-driven sampling (2006-2009). Individual-level characteristics and recruiter-recruit relationships were ascertained and merged with 2000 tract-level U.S. Census data. Descriptive statistics and population average models were used to identify correlates of membership in high HIV prevalence drug networks (>10.54% vs. <10.54% HIV). RESULTS: Individuals in high HIV prevalence drug networks were more likely to be recruited in neighborhoods with greater inequality (adjusted odds ratio [AOR], 5.85; 95% confidence interval [CI], 1.40-24.42), higher valued owner-occupied housing (AOR, 1.48; 95% CI, 1.14-1.92), and a higher proportion of Latinos (AOR, 1.83; 95% CI, 1.19-2.80). They reported more crack use (AOR, 7.23; 95% CI, 2.43-21.55), exchange sex (AOR, 1.82; 95% CI, 1.03-3.23), and recent drug treatment enrollment (AOR, 1.62; 95% CI, 1.05-2.50) and were less likely to report cocaine use (AOR, 0.40; 95% CI, 0.20-0.79) and recent homelessness (AOR, 0.32; 95% CI, 0.17-0.57). CONCLUSIONS: The relationship between exchange sex, crack use, and membership within high HIV prevalence drug networks may suggest an ideal HIV risk target population for intervention. Coupling network-based interventions with those adding risk-reduction and HIV testing/care/adherence counseling services to the standard of care in drug treatment programs should be explored in neighborhoods with increased inequality, higher valued owner-occupied housing, and a greater proportion of Latinos.
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