The majority of new HIV infections occur among high-risk groups such as men who have sex with men (MSM) and transfemales. Segments of these populations are also more likely to be economically disadvantaged. Economic disadvantage or low socioeconomic status has been linked to disease acquisition, morbidities and mortality. Interventions have been proposed to address these health issues through the reduction of poverty. However, this approach to intervention does not address underlying structural factors such as high HIV prevalence in impoverished neighborhoods and thus in social and sexual networks.

The present analysis examines low individual socioeconomic status and /or social and sexual network factors and their relationship to HIV acquisition risk among three populations at high-risk for HIV infection. Geographic analysis examined residential patterns and neighborhood patterns of HIV prevalence in San Francisco. Data collected in San Francisco from White MSM, Black MSM and transfemales were analyzed using Poisson regression to determine the factors associated with engaging in more episodes of potentially serodiscordant unprotected receptive anal intercourse.

As expected, transfemales and Black MSM were more likely to live in areas of higher HIV prevalence and lower income compared to White MSM. Interestingly the areas of higher HIV prevalence and lower income were also the areas with greater numbers of HIV prevention and care services. Black MSM and transfemales had lower socioeconomic status (SES) scores compared to White MSM. Black MSM were more likely to report serodiscordant partnerships and higher numbers of potentially serodiscordant unprotected sex acts. Decreasing SES and increasing neighborhood HIV/AIDS case density did not predict serodiscordant partnerships in any group. Increasing neighborhood HIV prevalence predicted an increase in the number of potentially serodiscordant unprotected sex acts among transfemales and Black MSM but only significantly so for transfemales.

HIV prevention interventions must also consider neighborhood factors such as neighborhood HIV prevalence in addition to considering individual level behavior change.