Available evidence does not support serosorting as an HIV risk reduction strategy: author's reply

Seroadaptation among men who have sex with men: emerging research themes

Seroadaptation describes a diverse set of potentially harm-reducing behaviors that use HIV status to inform sexual decision making. Men who have sex with men (MSM) in many settings adopt these practices, but their effectiveness at preventing HIV transmission is debated. Past modeling studies have demonstrated that serosorting is only effective at preventing HIV transmission when most men accurately know their HIV status, but additional modeling is needed to address the effectiveness of broader seroadaptive behaviors. The types of information withwhichMSMmake seroadaptive decisions is expanding to include viral load, treatment status, and HIV status based on home-use tests, and recent research has begun to examine the entire seroadaptive process, from an individual's intentions to seroadapt to their behaviors to their risk of acquiring or transmitting HIV and other STIs. More research is needed to craft clear public health messages about the risks and benefits of seroadaptive practices.

Interaction of mathematical modeling and social and behavioral HIV/AIDS research

PURPOSE OF REVIEW

HIV is transmitted within complex biobehavioral systems. Mathematical modeling can provide insight to complex population-level outcomes of various behaviors measured at an individual level.

RECENT FINDINGS

HIV models in the social and behavioral sciences can be categorized in a number of ways; here, we consider two classes of applications common in the field generally, and in the past year in particular: those models that explore significant behavioral determinants of HIV disparities within and between populations; and those models that seek to evaluate the potential impact of specific social and behavioral interventions.

SUMMARY

We discuss two overarching issues we see in the field: the need to further systematize effectiveness models of behavioral interventions, and the need for increasing investigation of the use of behavioral data in epidemic models. We believe that a recent initiative by the National Institutes of Health will qualitatively change the relationships between epidemic modeling and sociobehavioral prevention research in the coming years.