Social Network Blog - y7

This week’s readings deal with social networks and its effects on health. Cohen & Brissette’s article “Social Integration and Health: The Case of the Common Cold” looks at social integration, social network diversity and one’s susceptibility to the cold virus. Cohen and Brisette base social integration on “the numbers and types of social relationships, the extent of participation in social activities, or the perception of being an integrated member of the community” (2). Cohen and Brissette set out to investigate this by doing a study on the spread of the common cold in Pittsburg. They found that “social isolation constituted a major risk factor for the development of illness” (9) and while isolation accounted for the most of the effect, “the diversity of the network is more important than the number of network members and its association with colds is independent of the number of members” (8). They did not find, however that chronic stress had any effect on illness susceptibility (8).

This article seems to tie into Burt’s structural hole argument that we are better off with lots of weak, rather than strong ties, since it is the diversity, not the strength/quality of the tie that determines one’s susceptibility to illness. Though the article was written in 2000, the measure of social network diversity only included face-to-face and phone interactions. Q: What, if any, effects would the internet have on this study? One explanation for the results of this study is that social diversity might be mediated by the function of the endocrine and immune systems (9). The other explanation is that a more diversified social network would broaden the subject to more social controls and engage in improved health practices (10). Are these results dependent on more place-based, physical communities? Would the rise of virtual communities then also affect our health?

Dicken’s et. al’s article “Lack of Close Confidant, but not Depression, Predicts Further Cardiac Events After Myocardial Infraction” looks at the role of depression and the lack of social support before myocardial infarction (MI) in the UK. They had 2 main findings: (1) They failed to find an association between depression before MI and death rates and (2) Having a close confidant approximately halved the risk of having a subsequent cardiac event. Unlike Cohen & Brissette’s findings that the number and diversity of ties leads to better health, Dickens et. al’s study focused on the degree of intimacy of close relationships rather than the number of social contacts: “the close relationships may be protective of health and may promote recovery from depression” (521). While they did not determine the mechanism, some of they hypotheses included separation/association of lack of a close confidant, childhood separation & the rosk of ischaemic heart disease, or even a delay in seeking treatment if they are without a close confidant (521). This is a concern since a lot of the literature we have covered points to decreasing social capital, social networks and “close friends” we discuss important matters with. McPherson & Smith-Lovin discussed the dissolution of our core discussion networks and the loss of our broad scope of support. Not only is it affecting our social lives, but scholars like Dickens et. al prove that it is also affecting our health. However, this is not to say that this study doesn’t have its flaws. Firstly, it is hard to generalize the results of this study because it deals with a very small and select sample- elderly patients suffering from MI.

Bearman et. al’s article “Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks” looks at how “local preferences shape the macrostructures in which individuals are embedded and hence affect both the potential for disease diffusion and the determinants of individual risk” (45). The authors do this by examining the partnership patterns and network structure in a sample of students at a Midwest Highschool, dubbed “Jefferson High”, hoping to measure the key structural characteristics of a largely complete romantic and sexual network through which STDs may diffuse (45).

Their study showed some pretty surprising results. In contrast to popular myths, no “core group” of highly sexually active adolescents emerged but rather, were indirectly linked in long chains. Additionally, the number of sex partners proved to be less important than the position in a sexual network. However, as we have discovered through studies/articles like Kilworth’s “Estimating the Size of Personal Networks” in 1990, we still have little idea of the mean size of an informant’s network and little knowledge about our own social networks.

The authors discuss 4 models of infection, but found that the spanning tree most closely corresponded to the situation at “Jefferson HS”. A spanning tree is a “long chain of interconnections that stretches across a population, like rural phone wires running from a long trunk line to individual houses” (51). It is characterized by a graph with few cycles, low redundancy, and very sparse overall density, and most frequently observed in large and complex generalized exchange systems.

According to Bearman et. al, the network at “Jefferson”, “closely approximates a chainlike spanning tree” (52). The size of the large component of connected nodes is identified as the worst-case scenario for potential disease diffusion within the population. While there were many individuals at the end of the small branches in the large component with only one partner, “their risk for contracting an STD may be greater than an individual with multiple partners who is embedded in a smaller, disjoint component. Consequently, STD risk is not simply a matter of number of partners” (60).

While in theory, spanning trees are the most efficient structures for diffusion (since the absence of redundant lines maximizes reach at lowest density), the good news is that their efficiency is counteracted by their fragility- spanning trees are highly susceptible to breaks in transmission (79). Thus, from this study, we can see that the most effective strategy for reducing STDs or other disease diffusion rests on creating these structural breaks. According to the authors, it is not so much which actors are reached for an intervention, but that some are. This is because “given the dynamic tendency for unconnected dyads and triads to attach to the main component, the structure is equally sensitive to a break at any site in the graph” (80). While this seems to be useful in future social and health policy, it also highlights the how having “an accurate sense of the real structure of a network matters for the effectiveness of an intervention” (80). Is it feasible to investigate all the network structures of schools and change the social policies towards STD education accordingly? Does this only apply to the school system? How reliable is our present system of assessing network structures? Regardless of those issues, however, it is useful to know that sending out a broad, widespread message, including those on the periphery, may be equally, if not more, effective than targeting high-risk groups only.