Since shortly after World War II, when Ancel Keys and wife Margaret concluded that diets high in animal fat were the cause of cardiovascular diseases, an inestimable number of large, long-term studies have been conducted worldwide to look for proof of a causal relationship between the two. Despite a tremendous expenditure of time and money, all studies have failed to give an answer to the question of whether animal fats (primarily from red meat) and cholesterol cause cardiovascular disease. This failure is blamed on the inadequacies of epidemiology rather than the difficulties of proving a negative. When properly conducted, epidemiology has no inadequacies. Any failure of epidemiology is that of modern epidemiologists who equate association with cause.

Epidemiologic methods were developed decades ago to study the causes of epidemics. When Dr. Snow found an association of an outbreak of cholera in London with water from a specific public well, he had no means of proving his claim; microorganisms had not yet been discovered. So Dr. Snow had the pump handle removed from the offending well, which kept the well from being used. That ended the occurrence of new cases of cholera. This was proof that the water from that well was responsible for the cases of cholera. Thus, the phrase “removing the pump handle” is a tongue-in-cheek expression used to remind modern epidemiologists of this unconditional requirement of epidemiology.

Epidemiology is a tool used to evaluate whether an association exists between two sets of information or observation. This is a first step in an investigation to determine whether the two items may be causally related. If no association is found, there can be no cause-effect relationship. If there is an association, further investigation is required to determine if the relationship is causal.

The use of epidemiology was confined primarily to infectious diseases until the classic nutritional diseases of beriberi and pellagra convinced an unbelieving pubic that bad diets are capable of causing debilitating diseases. Then the tools of epidemiology were adapted to the study of nutritional diseases. However, nutritional scientists ignored the lessons from microbiology, which produced an elaborate system for proving that a pathogen associated with a disease was actually the cause of the disease. This system was called Koch’s Postulates.

The great complexities of how, what, and when bad nutrition causes chronic disease have discouraged epidemiologists from attempting to construct a “Koch’s postulates” for nutrition. This deficiency may now become less conspicuous because of a change in the method of interpreting statistical probability data that has come into use in recent years.

This new epidemiology absolves epidemiologists from conducting the final epidemiological step of proving that the association is a causal one, which is expensive, time consuming, and requires competence in the appropriate scientific discipline. This new method for reporting statistical associations no longer expresses the findings as probability of the existence of an association but rather as the risk of the potential event happening. This requires elaborate statistical manipulation of probability figures and converting them into statements of risk.

Thus, for example, study of factors associated with breast cancer would show a very high probability of an association of breast cancer with the wearing of skirts. By ignoring the lack of proof of a causal relationship and going straight to the risk evaluation, it can be shown that the risk of developing breast cancer from wearing of skirts is very high. This absurd example shows how easy it is for the new epidemiology to create the impression that a causal relationship exists in the absence of any scientific proof that it does by simply converting probability of association to degree of risk.

The new system of reporting an association as risk is claimed to be justified by the need to make difficult data understandable to the general public. Such a rationalization is only valid for studies in which a causal relationship has already been established. Obviously, if something cannot cause an event, there can be no risk that it could.

The silly example of breast cancer and skirts would fool no one, but the subjects of most nutrition studies published in scientific journals and reported in the public media are relatively involved and do not carry such public familiarity. For example, the health impact of eating red meat is not a subject on the tip of everyone’s tongue. Most people are unaware that there are no scientific data to validate the claim that consumption of red meat causes cardiovascular disease, cancer, or total mortality. Thus, there can be no such risk with eating (and enjoying) red meat. It is only the new epidemiology that says there is a risk.

Despite the deception it creates, the new epidemiology is employed by eminent epidemiologists whose papers are published in outstanding scientific journals. Thus we have influential scientific papers that report possibilities as probabilities:

… 9.3% of deaths in men and 7.6% in women in these cohorts could be prevented at the end of follow-up if all the individuals consumed fewer than 0.5 servings per day (approx. 42 g/d) of red meat. Conclusions: Red meat consumption is associated with an increased risk of total, CVD, and cancer mortality. Substitution of other healthy protein sources for red meat is associated with a lower mortality risk (¹).

The careful inclusion of “associated” does not lessen the impact of “risk” in the public mind. The reporting of the risk of mortality and CVD from consuming red meat when there is no scientific proof of a cause/effect relationship is irresponsible and immoral.

References:

1. Pan A, et al. Red Meat Consumption and Mortality. Archives of Internal Medicine. 2012; 172(7): 555-563.