Does Obesity Spread Through Friends? Christakis and Fowler's Critics

Take a statistical method that says obesity travels from friend to friend, point it at teenagers’ acne, and see what comes out. Ethan Cohen-Cole and Jason Fletcher did exactly that in BMJ in 2008, using 4,300 to 5,400 adolescents from the National Longitudinal Study of Adolescent Health (Add Health). With the usual friend-influence controls, they reported “significant network effects” for acne, headaches and height. A friend’s acne was linked to an odds ratio of 1.62 for your own acne (95% confidence interval 0.91 to 2.89). A friend’s headaches gave 1.47 (0.93 to 2.33). Height came out at a coefficient of 0.18 (0.15 to 0.26), which the authors read as a person’s height rising by 20% of a friend’s height. Nobody believes height is caught from the kid in the next seat.

The point was not that friends are irrelevant. The point was that a method which finds contagion in acne and height cannot, on its own, be trusted to find it in weight. That method is the foundation under a popular piece of habit advice: surround yourself with people for whom the behavior you want is ordinary. Habit advice often leans on the Framingham obesity paper for that claim. I could not reach the text of Atomic Habits to check how James Clear uses it, so this post does not quote the book. It looks only at the paper and at what four sets of researchers made of it.

What the 2007 paper reported

Christakis and Fowler (2007, New England Journal of Medicine 357:370-379) analyzed 12,067 people in the Framingham Heart Study network, measured repeatedly from 1971 to 2003. Body-mass index was available for everyone. The abstract reports:

• Clusters of obese people existed at every time point, and the clusters extended to three degrees of separation (a friend’s friend’s friend).
• The clusters “did not appear to be solely attributable to the selective formation of social ties among obese persons.”
• A person’s chance of becoming obese rose by 57% (95% CI 6 to 123) if a friend became obese in a given interval.
• Among adult siblings the figure was 40% (21 to 60), and among spouses 37% (7 to 73).
• The effects were not seen among neighbors.

Read the wording again. This is a 57% increase in the chance of becoming obese during an interval, not a 57% chance. And the confidence interval for friends is wide, from 6% to 123%. The paper is observational. Nobody assigned anyone a friend.

Retellings often compress this into “obesity is contagious,” then add that your friends’ habits become yours. The authors did write that obesity “appears to spread through social ties.” They did not measure habits, sleep, or any behavior. They measured body weight in people who had been recorded as friends.

Three ways the reading can fail

There are three explanations for two friends gaining weight together. A friend’s weight gain may change yours (the contagion reading). You may have picked a friend who already resembled you, in ways that also predict weight gain (homophily, which means similar people link up). Or you and your friend may share a gym closing, a new restaurant or a local economy (shared environment). Sorting these apart with observational data is hard, and economists call the general version the reflection problem, after a 1993 paper by Charles Manski that Christakis and Fowler themselves cite.

The four papers below each hit that problem from a different side.

Paper	What it did	What it found	What it cannot show
Christakis & Fowler, 2007, NEJM	Longitudinal models on the Framingham network (12,067 people, 1971-2003)	57% higher chance of becoming obese with an obese friend; clustering to three degrees	That the link is influence rather than similarity or shared surroundings
Cohen-Cole & Fletcher, 2008, Journal of Health Economics and BMJ	Replication in Add Health data; the same style of model on acne, headaches and height	Obesity effect shrank to indistinguishable from zero with standard controls; implausible “effects” appeared for acne, headaches and height	That the Framingham effect is zero (an estimate that is not distinguishable from zero is not a zero)
Lyons, 2011, Statistics, Politics, and Policy	Statistical critique of the published analyses	Argued the conclusions “do not follow” from the analyses	That friends have no influence; it targets the inference, not the idea
VanderWeele, 2011, Sociological Methods & Research	Sensitivity analysis: how strong would a hidden similarity need to be to explain the result away	Obesity and smoking cessation results fairly robust; happiness and loneliness less so; height, acne and headaches easily explained away	That any of the surviving effects is causal; it measures robustness, not proof

What Cohen-Cole and Fletcher did, and what they did not

Their Journal of Health Economics note (2008, 27(5):1382-1387) replicated the NEJM specification on Add Health and reported that point estimates of the “social network effect” were reduced and became “statistically indistinguishable from zero once standard econometric techniques are implemented.” The companion BMJ paper (337:a2533) is the one with acne, height and headaches.

One detail in the BMJ abstract deserves a careful read. The abstract calls the unadjusted effects significant, yet the printed intervals for acne (0.91 to 2.89) and headaches (0.93 to 2.33) both span 1. That is my observation about the numbers as printed, and it means those two were borderline at best. The height result, 0.15 to 0.26, was clearly nonzero. After adjusting for environmental confounders, the authors report that “the results become uniformly smaller and insignificant.”

Their conclusion was cautious: researchers “should be cautious in attributing correlations in health outcomes of close friends to social network effects.”

Christakis and Fowler answered the obesity note in the same journal issue (Fowler & Christakis, 2008). They made several points:

• Their Framingham estimates fall inside every one of Cohen-Cole and Fletcher’s confidence intervals, so the two sets of results could differ by chance.
• Cohen-Cole and Fletcher kept friendships fixed at the first survey wave, which counts people who stopped being friends and, in their view, biases results against finding an effect.
• They could not reproduce the Cohen-Cole and Fletcher results, and they report the authors declined to share code.
• In Add Health they found a directional pattern: the person who named a friend appeared to be influenced (p = 0.022), while the friend named by that person showed no effect (p = 0.90).

That last test matters, because shared surroundings should push both friends in the same direction, not just one. Christakis and Fowler also noted that the Framingham association did not decay with physical distance, which they read as evidence against local shared-environment explanations.

What Lyons argued

Russell Lyons, a mathematician, wrote a harsher critique (2011, Statistics, Politics, and Policy 2(1)). I read the arXiv version. His abstract says the conclusions “do not follow from Christakis and Fowler’s statistical analyses.” Several of his arguments are narrow and checkable. The directional comparison, he says, relies on intervals that overlap, so the observed differences “could be due to chance.” The lagged-obesity term the authors use to control for homophily, he argues, may amplify it rather than remove it. And the finding that associations do not fade with distance, in his reading, points toward homophily more than shared environment.

He also objects to how the three-degrees result is described. That analysis compares the real network to randomly generated ones with the same number of obese people, and he argues the percentages from it are not predictions about what happens if a friend gains weight. A flawed inference does not mean the opposite conclusion is true.

VanderWeele’s middle position

Tyler VanderWeele (2011, Sociological Methods & Research 40(2):240-255) took both sides seriously. His abstract grants the critique that “similar findings can be obtained when the same methodology is applied to height, acne and head-aches.” It then asks how strong an unmeasured similarity between friends would have to be to explain each result away. The answer differs by outcome. The obesity and smoking-cessation results are “reasonably robust to possible latent homophily or environmental confounding,” happiness and loneliness are “somewhat less so,” and the height, acne and headache effects “are all easily explained away.” I read the abstract only, not the full paper’s calculations.

So the acne result that opened this post is a useful negative control. The same machinery that makes acne look contagious also makes obesity look contagious, but VanderWeele’s check says the obesity result needs a stronger hidden confounder to disappear than the acne result does. That is evidence the obesity finding is not pure artifact. It is not evidence of the size of any causal effect, and it does not touch the question of whether the effect works through ordinary habits.

What survives, and what a reader can do with it

A fair summary of the literature as I read it:

1. Weight clusters in the Framingham network, out to three degrees. Nobody in these papers disputes the clustering. Lyons disputes how well the network was observed.
2. Whether a friend’s weight gain causes yours is unsettled. The authors of the original paper say yes with caveats, critics say the methods cannot tell, and the one sensitivity analysis I read says the obesity result is fairly hard to explain away without a strong hidden similarity.
3. None of these papers tested your situation. They did not study people deliberately joining a group, and they did not measure waking time, exercise or any chosen routine. Moving from “weight clusters among friends” to “join a group where the behavior is normal” is an inference the data never reach.

That leaves a practical question, which is how much weight to give friends’ influence when the best estimates range from “large” to “nothing detectable.” A few moves hold up under either reading.

First, treat the influence as real but unmeasured. You do not need a 57% figure to notice that you stay up later around certain people. You can watch your own behavior around them for two weeks.

Second, prefer evidence with assignment in it. Randomized peer-comparison experiments exist, such as the household energy reports that told homes how they compared with neighbors. They test a norm message directly instead of inferring it from who befriends whom.

Third, remember that your friends are a sample you only partly see. The friendship paradox arithmetic shows how network samples skew, and Lyons’s count of recorded friends shows how thin even a famous network dataset can be.

Fourth, separate absorbing a norm from choosing an obligation. A norm seeps in whether you want it or not, and the research above is about how big that seep is and whether it runs from friend to friend at all. An obligation is something you set up on purpose: one named person who will see proof. DontSnooze’s 30-second video proof sent to chosen friends is that second kind. These papers say nothing about how large its effect is, and they do not claim to. Who you pick matters too, since the closest friend is not always the best witness.

One number from the critique is worth carrying away. Even in the adolescent data used against the obesity result, Fowler and Christakis (2008) report that their own replication on Add Health still found a significant friend-weight coefficient (0.033, p = 0.022) when friendships were updated at each survey wave. Whether that is influence or similarity is the open question, and after four papers and one reply it remains open.