The impact of urban social life on sexual networks and health

Abstract

It probably comes as no surprise that sexual partnerships are not random; physical attraction, personality and shared values are just some of the factors that drive an individual’s selection of sexual partners. Although we are conscious of many of the factors that drive selection, sexual relationships – as with all social relationships – are shaped also by social norms and environmental constraints, some of which we might be entirely unaware of (Bearman et al. 2004). Furthermore, individual partnership decisions, driven by these preferences, norms and constraints, build upon each other to form complex patterns. When viewed at the population level, these complex patterns have substantial consequences for health and the spread of disease. This chapter details the norms, preferences and constraints that shape sexual partnership selection, how each of these interact with both physical place and population, and how individual decisions about partnership selection can create patterns that have substantial consequences for the spread of disease, particularly in urban populations. In discussing this process, we focus particularly on social network analysis, as it allows us to describe the specific relational mechanisms that lead to disease spread. We organize our discussion of these specific mechanisms into two distinct sections: network structural mechanisms and network compositional mechanisms. Following the discussion of these network mechanisms, we explore how social life, venues and online/mobile applications may impact sexual networks in urban regions, and then end on a brief discussion of limitations and future directions in this work. Each section surveys a wide range of literature on the formation and dissolution of sexual partnerships, and describes the impact of sexual networks on the spread of sexually transmitted infections (STIs) – in particular human immunodeficiency virus (HIV). We end with a discussion of how these network mechanisms are influenced by the real world and explore why, in order to understand disease dynamics, it is important to first have in-depth knowledge of the population of interest and the relevant social contextual forces impacting that population.