We can look more broadly than just changing behaviors, as we could do other things like vaccinate people against these pathogens or (in sci-fi land at the moment) somehow alter bodies to produce fecal matter free from pathogens or that dissolves when contacting the air or some other crazy solution.  The point is that there are many ways to deal with complex issues, and behavior change communication (or even behavior change!) are not always the answer.

However, in many cases, we do want to change a behavior as a way to address a larger issue.  It is important not to jump directly to behavior change communication, though, as this neglects much of the power of behavioral science.  A great recent example of this is a handwashing behavior change program in schools.  The goal was to encourage kids to wash their hands after using the toilet, but rather than run an educational campaign, the team altered the environment by creating a bright, painted path from the toilets to the handwashing station to draw the attention of the kids.  No one ever told the kids anything about the intervention, but handwashing rates went from 4% at baseline (with no infrastructure provided) to 18% by providing handwashing infrastructure to 68% with a footpath and painting (and even increased slightly 2 and 6 weeks later!).  Education could have complemented this intervention (currently being tested), and this doesn’t prove that education alone wouldn’t have worked (but lots of other poor results cast doubt on its effectiveness), but this certainly shows that changing behavior need not only involve education.

This is just a part of the power of my research group’s approach, Behavior Centered Design.  While the approach does include understanding the executive function of the human brain (the part associated with education and planning in most behavior change communication interventions), it also takes a comprehensive look at behavior motivations, “unconscious” actions, the role of the body, and the roles of the social, biological, and physical environment on producing behavior in “behavior settings.”  Solutions are more than just behavior change, and behavior change is more than just behavior change communication.

The bad news is that things are far more complicated than I imagined.  I thought that understanding the social determinants of health would essentially boil down to using something like Christina Bicchieri’s operational definitions of social norms to shed light on a norm and then following a set of proscribed procedures to change that norm through private education and/or public commitment.  This does seem a reasonable way to proceed in some cases, but what I’ve stumbled upon is that sociology has much more to say about the structure of society than this.

Harrison White’s Identity and Control serves as a great explanation of a multi-level network understanding of society, starting with the basic unit, not of the “person,” but of the identity.  This means that his basic unit for understanding society isn’t simply a human being, but an aspect of that personality that interacts with other identities.  For example, though I am a person, the identity I have as the author of this blog is quite different from my identity on a parenting website or a non-work-related social media profile.  White uses this example of our different internet accounts to explain how we switch between identities based on our contexts and that these bundles of identities and the networks we’re a part of ultimately form who we are as a person, whether online, with work colleagues, within our families, or as we sit on a bus with strangers.  So a simplistic understanding of the singular “network” of which we, as “persons” are a part, seems doomed to fail at a basic theoretical level.

But beyond this, the simplistic understanding of a network characterizing structural relationships as opposed to strictly individual attributes is also misleading.  Think, for example, of your alumni network.  If you meet a fellow alumnus at a networking event and he asks for your help, you don’t agree because of shared individual attributes (level of wealth, race, gender, etc.) or (necessarily) because of your direct network relationship (you know him through you friend who knows John who knows Suzie who knows Steve who knows him, though you may not know this series of connections at all!), but instead because of a shared identity that comes through an indirect sense of connection.  But surely this kind of connection is incredibly important, whether it’s an alumni network, supporters of a football team, religious affiliation, or being from the same neighborhood (in the form of a community with a shared identity, as opposed to simply a relationship defined by some geographic proximity).

All of the complexities of White’s theories are far beyond the scope of a blog post, but as I begin to unpack in my own mind how sociological theory can inform SNA for health, these ideas have tangible consequences for how we implement programs.  One of the most basic results that I’ve come across is a paper by Shakya et. al. (2014) that examines individual and village-level drivers of latrine uptake compared to the impact of the “network community,” defined in the paper by a strictly analytical method applied to whole network data to determine groups within which social influence is particularly strong.  The main result is that these communities have a stronger effect that individual- or village-level influences, which should bring to mind immediate implications for how we target programs to change behaviors that are socially influenced.

As we look beyond the individual to larger “social” determinants of health, let’s be sure to really dig into the rich understanding of social systems and processes that sociological theory can contribute and not just think about “culture” or peer-influences in their reductionist forms.

Michael Kremer and Edward Miguel, of Harvard and MIT respectively, co-authored a great paper in 2007 entitled “The Illusion of Sustainability.”  (First off, I must say that I like the fact that this is the title, not of an op-ed, but of a serious piece of original research.  Though it’s true that one paper can’t make the general point in a comprehensive manner, at least it shows a bit of savvy regarding how to market your research as opposed to giving others the opportunity to miss the point).  The study itself looks at how to make a deworming intervention “sustainable.”  Initially, children are given deworming medication for free, and then the results of peer influence in spreading the intervention are examined.  In addition, the authors investigate charging a low, highly-subsidized price, adding in health education, and using a mobilization strategy where people verbally committed to purchase and take the drugs.

The results read like the kind of thing that most would fear to publish: Peer effects are actually negative, meaning that those whose contacts received the drugs for free were less likely to take the drugs themselves.  Charging even a highly subsidized price drastically reduced the number of people taking the drugs.  Health education messages had no effect (though as a behavior change guy, I’m not surprised here and think it’s just more evidence for the need for behavior change communication).  The public commitments didn’t change a thing.  Total failure.

In this case though, the main point is that none of these strategies led to “sustainability.”  However, the drugs have been shown to more than pay for themselves in the benefit they bring to the community–they just don’t seem to have much private value, even if people get to experience them, learn about them from their friends, learn about their health benefits, or feel social pressure to follow through on their commitment.  The authors actually show that the deadweight loss from taxation would have to be unrealistically high, even for developing countries, to mean that their free provision by the government wouldn’t be a net benefit to a country.

Sure, it would be great if people would begin to purchase the drugs and use them of their own accord.  But ultimately, aren’t we seeking the biggest impact, not the trendiest labels we can put on our programs?  If we need to continue to subsidize drugs that have positive externalities to see the benefit or to include maintenance fees in our proposals instead of assuming communities will maintain their own roads?

I’m not arguing that encouraging sustainability shouldn’t be a goal, but it’s just bad policy to choose only projects that claim sustainability rather than demonstrating that more benefit could be produced by allocating 10% of project fees to ongoing maintenance.  What we really need is sustainability to be incorporated into a measure of program efficiency, rather than being a goal in itself.

What do you think?  Is sustainability the only thing that matters?  Or is it never really possible to have a truly sustainable program in the field?

Miller and Mobarak developed and tested an intervention where opinion leaders in the local community were given cookstoves, and then others to whom the stoves were marketed were told whether the opinion leaders accepted or rejected the stoves.  Unfortunately, those who knew that an opinion leader accepted the technology were more likely to have a favorable opinion of the stove, but no more likely to purchase one themselves.  However, if they knew that an opinion leader had rejected the stove, they were less likely to purchase one themselves.

Another study of deworming in Kenya hoped that when mass deworming medication was distributed to certain schools, it would not only spread to others by word of mouth but also through experiential learning due to observable spillover effects.  However, those whose contacts received deworming medication were ultimately less likely to purchase the medication themselves.  The authors suggest that an overly optimistic understanding of the permanence of deworming and the lack of observable private benefit for those dewormed resulted in these negative outcomes.

Both of these programs provide good examples of network effects that actually restricted uptake.  It’s important to remember that while networks will spread information, if the benefits of an idea are poorly understood or communicated or if people reject the idea, networks can also amplify those negative opinions as well.  In part 2, we’ll examine additional ways that networks can be harmful and discuss why this is the case.

There are many different methods for finding the most influential individuals, ranging from surveying the entire network and computing them to simpler methods such as asking those in influential positions or friendship nomination.  A recent study looked at basing their intervention targets on mapping out the whole network, computing how many connections each person had (technically, in-degree centrality), and choosing those with the most connections.  This intensive method, one of a few tested, actually didn’t improve outcomes compared to a random selection of individuals.  There could be many reasons for this, but one of the main ones in my opinion is that this is the wrong way to think about targeting.

The authors don’t totally disagree–they recognize many of the pitfalls of this method as well.  Targeting the most connected individuals may tend to target those connected to each other, meaning that parts of the network separated from the most well-connected because of sociodemographic differences or even just spatial distance may be quite far from a targeted individual in terms of the number of ties an intervention must pass through to reach them.

A more promising approach, described by Stephen Borgatti (2006), is to identify “Key Players” in the network.  Rather than just computing a set of people who are the most central to a network, the KPP-Pos algorithm he describes selects a set of individuals such that the distance from individuals in the network to their nearest Key Player is minimized.  Borgatti’s algorithm is based on computing a “distance-weighted reach” of a selected set of nodes, so that how far nodes are from the overall set is considered rather than just looking at the most connected nodes.

A simple example where this might be useful is shown below, where 1 and 2 are the most central individuals based on degree centrality (5 ties each), but if you want to target two individuals, selecting 1 and 7 would yield a better coverage of the network through direct contact with those targeted.  Though this diagram seems a bit contrived, homophily is so strong in networks that similar results are not uncommon in real world data (and two real-world data sets are tested by Borgatti).

It seems a rather straightforward idea, and certainly more complex techniques have been developed since this technique was published, yet the authors of the above study either seem unaware of this technique or think it not so obviously superior to methods used in the study as to merit inclusion.

A question that comes to mind based on these techniques is: Is targeting multiple individuals connected to the same people more effective than targeting just individual Key Players?  Certainly it seems that targeting a group would be more effective than targeting individuals, but is the difference worth the smaller number of groups that could be targeted for a given project cost?  If you know the answer, I’d be happy to hear about it!

Interested in learning more about SNA or how it can improve your programs?  Contact me directly or post a comment below!