What sacred crocodiles and meat festivals can teach us about evidence-informed health policy

Last week my colleagues and I published a paper in Implementation Science: “Exchanging and Using Research Evidence in Health Policy Networks: a statistical network analysis.” We set out to understand which network factors might increase the likelihood that actors exchange research evidence with each other. Here's why this is important:

• When people exchange evidence, they are more likely to use evidence,
• The use of research evidence can help improve the effectiveness and equity of health policies, and;
• Evidence exchange is a social process and thus should be studied using methods that enable the measurement and modeling of the social dimension.

Using exponential random graph models, we found that network structure, more than individual attributes, explained why people provided or requested evidence in their policy networks, and whether they used it to inform their own decision-making.

To help explain these findings, I provide some anecdotes from my life in Burkina Faso.

Point 1: Leverage existing relationships

People in Burkina Faso are entrepreneurial. My good friend Ai is young but has held any number of roles as research assistant, language instructor, translator, guest house owner, CEO, etc. How does Ai succeed in these roles? In addition to her intelligence, she understands the power of her networks and the importance of trustworthiness.

People's networks are large, and effective. It would take 20 minutes to drive 100 meters with Mme Salimata Ki, my mentor in the Ministry, as she knew everyone we drove past, and stopped to either have a conversation with them or give them food or money if they needed it. My access to respondents was thanks to her, and the woman could move mountains. I would love to map her network. Actually, it reminds me of a study underway by JP Onnela at Harvard where they hypothesize that the structure of participants at the Kumbh Mela replicates the global network structure in India. Salimata is the Kumbh Mela of Burkina Faso. 

The point is: Burkinabe’s are used to playing many roles in their networks. We found that policy actors were more likely to exchange evidence with each other if they had other types of relationships together. I called this ‘layering’ in the paper but it is also called multiplexity in network terms.

What can we do? If you have evidence to disseminate, encourage people to share it with their friends, colleagues, officemates, family, tailor, butcher, etc. Journal websites do this by allowing users to share on social media, but we know that face-to-face ties – strong ties – are more likely to lead to useful knowledge exchange because they help make information 'sticky.' So instead of the Facebook icon, let’s make a ‘talk’ icon and put it on evidence outputs. Of course journal clubs are perfect for this. 

Point 2: Don’t eat too much street meat

There can be too much of a good thing, even in the world of grilled meats and evidence exchange. On the meat front, I attended the annual Festivale de grillades (grilled meat festival) in Ouagadougou with a close friend from the U.S. There was an entry fee, so we were incentivized to eat as much as possible. We egged each other on to eat and eat, to the point that we began to perspire and felt dizzy, and this lasted for at least 24 hours. I believe clinicians refer to this as "meat sweats." I doubt we would have succumbed to the meat if we had been with a more diverse group of friends. Salimata would not have let me eat so much street meat!

In our study of evidence exchange networks, one policy issue – community integrated management of childhood illness -- showed much higher frequency of exchange (and use) of evidence than the other issues. This network also showed a propensity to form cliques, aka triangles. Triangles represent a social process that we observe in networks at a greater frequency that can be explained by chance alone: people are more likely to form a relationship if they have a friend in common. When I see a network with lots of triangles, I think, "empirical signature of cohesive and congruent policy communities!" (or, female adolescent friendship network). Indeed, analyses from my other chapters show that despite all this exchange and ‘use’ in this network, evidence wasn’t used instrumentally to solve tough problems about child health. It was used symbolically to justify pre-determined policy positions, mainly of development partners and donors. In other words, people came together to eat meat, it was tasty (i.e., the Lancet Child Survival series), they ate a lot, but eventually it became difficult to discern whether the meat still tasted good or  they continued to eat because everyone else was.

In other words, we need to support evidence exchange and use, but we also need to make sure that people have the capacity to find and interpret the evidence and that network structure does not encourage group-think over innovation.

What can we do? Try to avoid attending meat festivals with your best friend. Instead, go with a diverse group. Invite some vegetarians! You will be more likely to try new things, and your stomach will thank you. The National AIDS Council (CNLS-IST) did this in the early 2000s by requiring representation of civil society and PLHIV, ensuring the diversity of the Council and entrenching the capacity and access of civil society organizations in the HIV policy domain. This has had a lasting positive effect on the HIV policy network structure and its effectiveness in Burkina Faso; we found the HIV actors were more likely to use evidence when it was provided to them, and this network ultimately produced and used evidence to instrumentally achieve policy change. 

Point 3: Use person-net, not Internet

If I had a nickel for every person (from North America and Europe) who said, “Oh! You’re studying Facebook in Burkina Faso,” I’d have about 85 cents (which is significant). Social media and online social networking is exciting, but not very useful in Burkina Faso circa 2011.

Poor internet infrastructure poses one of the biggest challenges to the fidelity of many of the knowledge transfer, policy translation, and implementation science interventions that are shipped off to West Africa and elsewhere. When I arrived in Burkina in 2011 I was kindly given an office in the Ministry of Health. I had no internet where I was living, there was really no public internet (although I single-handedly supported the tonic water industry in hotel lobbies), and the connection at the Ministry was really slow. It was virtually impossible to read emails, much less download a research article to inform policy with. So I looked around my office and realized that if I bypassed the wifi router and plugged the Ethernet cord directly from the wall into my computer, the internet performed much faster. Did it cross my mind that this router might be used by other offices? Briefly, but no one said anything. And then ONE MONTH LATER someone knocked on my door and asked whether they could look at the router. I had turned off the internet to the entire Department. 

Other common reasons for internet outages included:

• Deep-sea underground digging in Guinea or Cote d’Ivoire had cut the internet cable. THE internet cable. For the continent.
• The popularity of copper jewelry meant that local internet cables were often dug up to make bracelets.

Needless to say, downloading research articles is not high on your list of things to do when you have a brief window of connectivity. I found that this meant that certain actors (i.e., development partners) had better access to evidence than others, leading to the inequitable distribution of normative power.

What can we do? We should be much more creative about the media we use to disseminate research evidence. Knowledge brokers, policy dialogues, printed policy briefs are all possible examples. Salimata puts research evidence on a CD to share with colleagues. Evidence at your fingertips. 

Final point: My network made me do it!

I shook hands with a crocodile in Burkina Faso. My personal attributes – risk preference, IQ, education, past experience – would not have predicted such a thing. But my husband encouraged me, and Salimata facilitated it, and so I went to a village of sacred crocodiles and sat on one. We know about social influence and heuristics in decision-making – both came into play in my case -- but what is even more compelling, and also more abstract, is the role of social structure on our decisions. That is, we are influenced not only by what our friends are doing, but how are friends are connected to each other and to others. And this is the main point of our paper. Controlling for individual attributes and shared traits, whether or not two people exchanged evidence was best explained by their network's structure.

What can we do? As noted above, we can design networks that are more conducive to appropriate evidence exchange and use. I'm beginning to work towards this with NetworkRx, a very beta simulation tool to see the effect of network structure on policy outcomes. And stay away from crocodiles.

Hope you find the paper delicious.

Who is the Kevin Bacon of Health Systems Research?

Earlier, I wrote about network mapping at the Third Global Symposium on Health Systems Research. We mapped participants' social ties, collaboration ties, and information seeking ties as reported by them on an online survey. 

Today I will show you the collaboration network generated through this process, as well as the 'real' network of co-authorship relationships generated through data mining. 

We asked: "With whom have you collaborated on health systems or policy research in the past two years." Here's what we got: 

In this graph, 91% of all nodes belong to a single, connected component. And, any two people in this network are connected by four or fewer people. We knew that the HSR community was… well-connected… (insular also comes to mind), but really? 

In network science this is referred to as the 'small-world phenomenon.' Ever heard of 'six degrees of separation'? This refers to the fact that any two people in the United States are connected by six or fewer acquaintances (as confirmed by the famous postcard experiment of Stanley Milgram). Or that everyone in the movie world is connected to Kevin Bacon* by four or fewer connections (you might have also heard six). 

In other words, Kevin Bacon is connected to the most other actors in the fewest steps. Why does this matter? Imagine that you had an important message to deliver to everyone in your network, and that the message travels from person to person starting with one person. Who would you give it to for fastest diffusion? Test your intuition here by trying to slow the spread of an infectious disease in a network. 

Bottom line, you want to find the person who can access different parts of the network in the fewest steps. They sit on paths between the most number of people. They have the highest betweenness centrality. 

But, our network data were not complete. Only about 70 people completed the survey, so we have plenty of missing edges and attributes for those who didn't complete the survey. 

This begs two questions: 

1. Can we mine citation data to get a fuller picture?

2. Who is the Kevin Bacon of HSR?

To answer the first, yes, we can use publicly available citation data. I searched ISI/Web of Science for all the names of Health Systems Global members (which was our proxy for who attended the conference), and downloaded the names of everyone they have published with since 2012, mirroring the survey question. Using Sci2, I constructed a co-authorship network with our respondents and their co-authors as nodes, and co-authorship relationships as edges/ties. 

This is the network we get: 

Complete co-authorship network of survey respondents

This network has 5576 nodes, representing 1433 publications since 2012. 75% of these authors are connected in one large component, with a relatively dense core but sparser periphery structure. We also see many more components -- a total of 191 unconnected smaller networks. No longer is everyone as connected. Now, like in the real world, everyone is connected by 6 degrees of separation, compared with 4 above. 

Who has the shortest paths to everyone else in the network? Who is our Kevin Bacon?

Co-authorship network of survey respondents, nodes sized by (scaled) betweenness centrality, 

six highest betweenness scored labeled 

John Lavis has the highest betweenness centrality. John** was on the greatest number of paths between other actors. He is in a position to best connect otherwise unconnected parts of the network, and to reach all other nodes most efficiently, all else being equal.

When it comes to ideas, John Lavis is a broker (which is appropriate, as he studies knowledge brokers). He can:

• Access new ideas from diverse parts of the network

• Disseminate new ideas to diverse parts of the network

• Act as a translator between groups

What makes John different than the rest of us? Since 2012, he published 30 articles with 78 different people. That's an average of 2.6 new co-authors per paper! Imagine how many people they are connected to. And the fact that you might not recognize all the names is a good thing for innovation. 

Does he intentionally try to broker the world of HSR? Maybe (we should ask him), but he also has many factors in his favor: he's an expert, so diverse authors seek him out and he's based in Canada where institutions are smaller and thus one cannot rely on within-institutional collaborations alone. 

Did you happen to notice that of the six highest scores in the HSR network above, four are Canadian by birth? Natural peacekeepers?

Back to the big picture. What does this mean for the field of Health Systems Research?

Connectivity is a good thing -- it's how we share and interpret ideas and knowledge, access resources and opportunities, and function as a society -- but there are two concerns with connectivity.

1. More connectivity isn't always better

2. The distribution of connections matter

First, there is a danger of networks becoming overly connected. Multiple, redundant connections with the same groups of people (represented by high network density or many triangles in a network) leads to 'group think' and may stymie innovation. Densely connected network cores begin to operate like an echo chamber.

Second, we need to think of how the connections are distributed across network nodes. At the node level, centrality (i.e., number of connections) equals social capital. We learned that betweenness centrality is one particularly powerful measure of capital, brokerage, and potential influence. The HSR co-authorship network had extremely large variation in people's betweenness scores, meaning that there is a lot of "power" in this network, and it is not evenly distributed.

So high network density is bad? 

No. We need to separate connectedness into two components: density (i.e., the proportion of actor pairs who are connected) and centralization (i.e., the distribution of connections across the network). Density in and of itself isn't bad; sometimes all those repeated connections are necessary. It is the centralization of those connections around one or a few nodes that is not ideal for innovation. We used to think that density and decentralization were at odds but we now know they can co-exist.

Reza Yousefi-Nooraie found that academic medical research teams were more productive if their networks were dense, but also decentralized and open to external networks. A recent study in Nature, corroborating other studies of knowledge-intensive organizations, found that dense networks aid knowledge transfer when the knowledge is complex. The same study also found that team performance was positively associated with the strength of expressive ties (i.e., friendships).

However, I found in Burkina Faso that while dense networks aided the dissemination and spread of research evidence (i.e., complex information), the same density protected the policy status quo and prevented the new ideas from being adopted.

So what does success look like for a HSR network? An ideal academic co-authorship network would be productive (i.e., publish a large quantity of research) but it should also be innovative (i.e., willing to adopt new ideas) and relevant (i.e., research priorities are identified systematically and democratically and research findings are used). What would that look like as a network?

• Dense, with strong ties, including layered ties based on friendship and trust

• Decentralized, with brokers at the margins who can access external networks and new ideas

• Diverse, with varied representation of people who can translate across communities

How do we move towards such a network?

 

As individuals, we can continue to develop strong relationships with co-authors that lead to intellectual productivity. The HSR network, particularly the core, could be more decentralized. We could all be better at developing new collaborations with people outside of our existing networks. Finally, but related to the previous point, we can do better at building diverse research teams, as measured by geographic location, sex, age, discipline, and job role. At the network level, there are interventions that could be applied, but I can't think of examples where co-authorship networks are actively governed. What about research collaboration networks? Is this the role of Health Systems Global (or donors)?

* Note that newer data show that Sean Connery is on more shorter paths than Kevin Bacon.

** John was my PhD supervisor, but this in no way informed the network analysis.