Confounding Data Interpretation: Many of the comorbidities correlated with weight can be explained by other factors and/or other factors have not been considered or ruled out in the studies. A lot of studies assume that all fat people do not exercise and all thin people do exercise. Thus, these factors are confounded with studies of weight making comments about activity levels and studies of activity levels making comments about weight control. Diet gets confounded in the same way. BMI has become a short-cut not only to assessing someone's health, but to assessing someone's health practices. But in studies where such factors as activity level, consumption of certain kinds of food, social factors such as socioeconomic levels and stress levels have been accounted for, weight becomes an almost non-existent factor.

Biased Funding: So, why in the face of the two above points, does the science get so misreported and misunderstood? Money. And that is the third contention. Much of what the media reports is not science at all, but is reported as if it is science.  "Studies have shown..." are magic words in our public discourse. But much of what is reported comes from press releases by people with vested interests in the public believing certain things. Knowing who funded what is an important component in judging the accuracy of findings. Biases exist in all research. That doesn't mean that all research is bad. It means that an informed reader of the research needs to know the biases in order to judge the usefulness of the information. This is especially true of the so-called "cost analysis" that has been done. Digging into these studies about how much obesity is costing the United States and you will find companies like Allergan who doubled their market based on that panic alone.

So why are these points important? Are scholars who raise these points just ignoring important correlations by repeating their own magic words "correlation is not causation"?

Establishing Causation

An education in cause and effect might help put this in perspective. Very few things are proven as causing other things. We take some things for granted as causes, but in science one makes a case for cause, one does not prove (except in very limited ways in laws of physics, for example). Issues of reliability and validity are important in making these cases. Reliability means that the study is replicable and can be conducted repeatedly in the same manner as before, preferably by other people to reduce bias. Validity means that the study is actually measuring what it is assuming it is measuring.

Reliability and validity are extremely difficult to achieve in human studies. Unlike chemical and biological processes that can be controlled within laboratories, studying humans has the added complication that the humans can figure out they are being studied and shift results. Yes, studying cells and chemical reactions in human bodies is easier than studying behavior, but there are still problems, given the extent to which human contact with the environment and the human aging process changes those chemical and biological processes constantly.

But even if a strong case can be made for reliability and validity, three conditions must be satisfied to demonstrate cause and effect (essentially to strengthen the case for it). These conditions are all necessary but no one of them is sufficient:

1. The cause has to occur in time before the effect.
   
2. Changes in the cause has to create a corresponding change in the effect.
   
3. NO OTHER EXPLANATION for the relationship can be present.

Timing

This sounds basic and easy to demonstrate, but if you think about it, especially in regards to humans, timing is difficult. For example, if fatness were to cause these comorbidities, the fatness has to occur in time before the diabetes, high blood pressure or heart disease. But when exactly did these medical conditions occur? Not at the point of diagnosis because symptoms usually are present before a diagnosis is sought. Not at the point of symptoms because often people realize they have been sick longer than they knew. What if the case can be made that there is a genetic component? Can the disease said to have started in the womb? What if the person loses and gains weight multiple times? When in time was the weight a factor? This complexity is frequently ignored in studies, making almost every study done problematic in making a case for cause and effect.

Correlation

This is the darling of the media mostly because it has numbers that lend a false sense of precision. I remember as a reporter keeping several calculations in a file to be used when discussing taxes or other such topics because it was important to report the numbers in a specific way that would tantalize rather than bore. It's tricky in reporting. Sensational numbers are better than small, hard to understand or overly large, beyond comprehension numbers. Percentages work better than totals. Statistical assessments of correlation are easily reported in percentages and thus often make the first paragraph or even the headline.

Correlations are a necessary part of demonstrating a cause and effect but they are not sufficient and as such it is important to seriously review correlations to understand what they do and do not mean. I know of no one in HAES who denies that correlations between weight and certain medical conditions exist. No one is denying correlation or ignoring it. To the contrary, it is important to understand exactly what these correlations mean. Were they arrived at with good data? Are they reliable? Do they measure what they suggest they are measuring? These are the questions that other scientists and scholars must ask when confronted with such important data. Journalists do not ask these questions. Journalists report sensational numbers and rely upon the researcher who came up with the number to tell them what it means. Thus, reporting of correlations are instantly biased in two ways -- towards the sensational and towards the producer of the research. In peer review journals, it is not the researcher who interprets or reviews the data, it is his or her colleagues. This reduces bias.