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In an entertaining and informative piece in The Conversation, Jon Borwein and Michael Rose look at the dangers of making a link between unrelated results. “Here’s an historical tidbit you may not be aware of,” they write. “Between the years 1860 and 1940, as the number of Methodist ministers living in New England increased, so too did the amount of Cuban rum imported into Boston – and they both increased in an extremely similar way. Thus, Methodist ministers must have bought up lots of rum in that time period! Actually no, that’s a silly conclusion to draw. What’s really going on is that both quantities – Methodist ministers and Cuban rum – were driven upwards by other factors, such as population growth. In reaching that incorrect conclusion, we’ve made the far-too-common mistake of confusing correlation with causation.”


As we are reporting on a number of large prospective studies and their correlations (otherwise known as associations) in this issue of GI News, we thought we would kick off with an extract from a post by Prof Arya Sharma (Even Correlations Based on Billions of Data Points Do Not Prove Causation, Obesity Notes, August 23, 2017) reminding us of the very serious limitations of such studies.

Even Correlations Based on Billions of Data Points Do Not Prove Causation 
Readers may have already heard about a recent study by Tim Althoff and colleagues from Stanford University, published in Nature, that analyses physical activity data collected from smart phones consisting of 68 million days of physical activity for 717,527 people, in 111 countries (only 46 of which were included in the study). As one may expect, not only do activity levels vary widely across countries but also substantially within countries (which in general terms, the authors refer to as “activity inequality”). It turns out that activity inequality and not actual levels of activity predict obesity rates (based on BMI).

The authors discuss [in their paper] various limitation of their study but fail to mention the biggest limitation of all, the simple fact that correlations, no matter how strong or how large the data set, simply cannot prove causality.

Thus, while the data does prove the point that you can do all sorts of interesting analyses when you have large data sets, it simply does not prove that activity levels (or activity inequality for that matter) actually has much to do with obesity at all. Indeed, one could think of a number of confounders that would otherwise differentiate countries with high activity inequality that happen to have high obesity rates from countries that have low activity inequality and low obesity rates (let’s not even mention reverse causality).

Thus, as nice as the figures presented in the paper may be, it is really hard to follow the authors’ conclusion that, ‘Our findings can help us to understand the prevalence, spread, and effects of inactivity and obesity within and across countries and subpopulations and to design communities, policies, and interventions that promote greater physical activity.’

This is not to say that designing communities, policies, and interventions would not be of substantial health benefits – given all of the known benefits of physical activity. Unfortunately, whether or not, these policies would do anything to prevent or reverse obesity is another matter altogether and remains as unclear after this study as before. 

 Dr Sharma 
Dr Sharma is Professor of Medicine and Chair in Obesity Research and Management at the University of Alberta, Edmonton, Canada. He is also the Clinical Co-Chair of the Alberta Health Services Obesity Program. He has authored and co-authored more than 350 scientific articles and has lectured widely on the etiology and management of obesity and related cardiovascular disorders and is regularly featured as a medical expert in national and international TV and print media and maintains a widely read obesity blog at