In some cases, Bradford Hill said it’s possible to relate the level of exposure to the risk of disease. For instance, the more cigarettes a person smokes, the more likely they are to die from them. What’s more, it may be possible to draw an analogy with a similar cause and effect, such as another chemical that causes cancer. Finally, Bradford Hill suggested it’s worth checking to see whether the cause is biologically plausible and fits with what’s already known to scientists.
Bradford Hill emphasised that these viewpoints were not a checklist to ‘prove’ something beyond dispute. Rather, the aim was to help answer a crucial question: is there any better explanation for what we are seeing than simple cause and effect? As well as providing evidence that smoking caused cancer, these kinds of methods have helped researchers uncover the source of other diseases. During the 1950s and 1960s, epidemiologist Alice Stewart gathered evidence that low-dose radiation could cause leukaemia.[41] At the time, new X-ray technology was regularly being used on pregnant women; there were even X-rays in shoe shops, so people could see their feet inside the shoes. After a long battle by Stewart, these hazards were removed. More recently, researchers at the US CDC used the Bradford Hill viewpoints to argue that infections with Zika were causing birth defects.[42]
Establishing such causes and effects is inherently difficult. Often there will be an intense debate about what is responsible and what should be done. Still, Stewart believed that, faced with troubling evidence, people should act despite the inevitable uncertainty involved. ‘The trick is to get the best guess of the thickness of the ice when crossing a lake,’ she once said. ‘The art of the game is to get the correct judgment of the weight of the evidence, knowing that your judgment is subject to change under the pressure of new observations.’[43]
When christakis and fowler originally set out to study social contagion, they’d planned to do it from scratch. The idea was to recruit 1,000 people, get each of them to name five contacts, and then get each of their contacts to name five more contacts. In total, they would have had to track the behaviour of 31,000 people in detail for multiple years. A study that large would have cost around $30m.[44]
While exploring options, the pair got in touch with the team running the Framingham Heart Study, because it would be easier to recruit those initial 1,000 people from an existing project. When Christakis visited Marian Bellwood, the project co-ordinator, she mentioned they kept forms in the basement with details of each participant. To avoid losing contact with participants, they’d got people to list their relatives, friends and co-workers on the forms. It turned out that many of these contacts were also in the study, which meant their health information was being recorded too.
Christakis was astonished. Rather than recruiting a completely new set of social contacts, they could instead piece together the social network among Framingham participants. ‘I called James from the parking lot and said, “you won’t believe this!”,’ he recalled. There was just one catch: they’d have to go through twelve thousand names and fifty thousand addresses to identify the existing links. ‘We had to decipher everyone’s handwriting,’ Christakis said. ‘It took two years to computerise it.’
The pair had initially thought about analysing the spread of smoking, but decided obesity was a better starting point. Smoking depended on what participants reported, whereas obesity could be observed directly. ‘Because we were doing something so novel, we wanted to start with something that could be objectively measured,’ Christakis said.
The next step was to estimate whether obesity was being transmitted through the network. This meant tackling the reflection problem, separating potential contagion from homophily or environmental factors. To try and rule out the birds-of-a-feather effect of homophily, the pair included a time lag in the analysis; if obesity really spread from one person to their friend, the friend couldn’t have become obese first. Environmental factors were trickier to exclude, but Christakis and Fowler tried to tackle the issue by looking at the direction of friendship. Suppose I list you as a friend in a survey, but you don’t list me. This suggests I am more influenced by you than you are by me. However, if in reality we’re actually both influenced by some shared environmental factor – like a new fast food restaurant – our friendship direction shouldn’t affect who becomes obese. Christakis and Fowler found evidence that it did matter, suggesting that obesity could be contagious.
When the analysis was published, it received sharp criticism from some researchers. Much of the debate came down to two main points. The first was that the statistical evidence could have been stronger: the result showing that obesity was contagious was not as definitive as it would need to be for, say, a clinical trial showing whether a new drug worked. The second criticism was that, given the methods and data Christakis and Fowler had used, they could not conclusively rule out other explanations. In theory, it was possible to imagine a situation involving homophily and environment that could have produced the same pattern.
In my view, these are both reasonable criticisms of the research. But it doesn’t mean that the studies weren’t useful. Commenting on the debate about Christakis and Fowler’s early papers, statistician Tom Snijders suggested that the studies had limitations, but were still important because they’d found an innovative way to put social contagion on scientists’ agenda. ‘Bravo for the imagination and braveness of Nick Christakis and James Fowler.’ [45]
In the decade since Christakis and Fowler published their initial analysis of the Framingham data, evidence for social contagion has accumulated. Several other research groups have also shown that things like obesity, smoking, and happiness can be contagious. As we’ve seen, it is notoriously difficult to study social contagion, but we now have a much
