The 17th century poet, John Donne, famously wrote, “No man is an island, entire of itself; every man is a piece of the continent, A part of the main.” In reality, even an island is not an island – structurally or functionally. Islands are separated (or connected) – depending on perspective or purpose – to continents by the water and air. The nature of these connections drives the evolution of island “phenotypes.” Analogously, human phenotypes are driven by the information flows and the feedback systems that course through our networks. The study of networks has shown that human activities exhibit regularities that lead to networks with special features and a few primary features of networks yield enormous insight into human phenotypes. Understanding network structure can also yield insights into how information and resource flows begin, transmit, and progress within and across networks. Humans are referred to as social animals – this is not just an idle observation or a fashionable claim, but an empirical and foundational truth of human nature.
The Centers for Disease Control (CDC) defines social determinants of health (SDOH) as the “conditions in the environments where people are born, live, work, play, worship, and age.” These social determinants transmitted via our social networks account for 60% of the variation in health outcomes. The life altering diseases of modernity – diabetes, cancer, heart disease, stroke, substance abuse, and neurocognitive disorders – and the supposed diseases of antiquity – infectious diseases – are intimately associated with our social networks. Our various interactions – friendship, kinship, resource-exchange, information-exchange, or casual contact – coalesce into robust and typical patterns that can influence, reinforce, and hem us into maladaptive phenotypes. These phenotypes affect us by not only increasing the risk of contracting a disease but also the outcomes associated with it. Prevalent network patterns such as assortative networks, modular disassortative networks, and scale-free networks all have typical structures and characteristics. Information flows through them in type specific ways, they change and are resistant to change predictably. If we understand these features and properties of networks, then it should follow that we can design disease treatments & interventions that accounts for network information.
Two of the most widespread and dominant features of human networks are homophily and clustering. Homophily – commonly memorialized as ‘birds of a feather, flock together’ – is the general tendency for individuals to be connected to similar others. Clustering is how likely two people connected to you are also connected to each other. Homophily and clustering occur because either members select partners who are similar to themselves (social selection) or the behavior of network partners exert influence and behaviors converge over time (social influence). In general, homophily and clustering are so widespread and powerful because they decrease the uncertainty and friction associated with social living. It is easier to communicate, understand, and predict the behavior of people with similar world views. You can use heuristics of similarity or familiarity to assess a strategy rather than expending resources and energy to evaluate the hypothesis on your own. The downside of homophily is that it can also reinforce and entrench maladaptive strategies. Behavioral tendencies such as diet, smoking, physical activity, infectious disease transmission, and cancer screening have all been shown to propagate by the social network through the mechanisms of homophily and clustering.
As discussed in my last essay, the complex and resistant diseases of modernity require an ecological framework. There isn’t a single cause or even a single system that causes diseases such as diabetes, cancer, or heart disease. These diseases are multifactorial and multidimensional. Their causes reside not only in multiple systems at different levels, but also in the interactions between them. The systems above – as represented by social networks – are potent drivers of human health that directly and profoundly impact the systems below (next essay). Any complete theory of disease attribution should factor in social networks. The payoff will be more accurate predictions of disease onset and progression, which in turn can help devise more complete strategies of prevention and disease burden reduction.