The devil is always in the detail but because people today only want sound-bites as a basis of understanding the world around them, devious manipulation is possible. People will tell you they have neither the time nor the inclination to study the detail of anything but in doing so they are actually believing what they are told in soundbites instead. So if you like your information in one short snappy sentence that is easy to remember, you won't enjoy the following.

Each and every time I am locked in debate with any of the many representatives of anti-smoking, they will always resort to the same tactic. Regardless of what aspect of smoking we are discussing they will say with confidence that they have research evidence that proves what they are claiming, no matter how absurd that claim may be. Because any true clinical trials on smokers actually smoking tobacco is considered unethical, it is never done so the alleged evidence in each case is statistical, (if it exists at all). The scientific name for this branch of science is called epidemiology. It is the use or mis-use of epidemiology that damns us smokers to hell so we need to understand it.

Sensing always that the so-called evidence could be a con-job or at least unreliable, I have been trying to learn more about this epidemiology. For this reason I am grateful to Alice and Fred Ottoboni for their excellent article entitled, "The New Epidemiology." 

It is worth quoting their observations to you. They say that, "Epidemiologic methods were developed decades ago to study the causes of epidemics. When Dr. Snow found an association in the 1830's 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." Thus also, epidemiology became a very useful tool for examining problems with a single cause, (or mono-factorial cause). Take note though that it was never designed to prove causes of any multi-factorial diseases such as heart disease or cancer. Associations might point to a trend perhaps but to discover the real cause or causes would take years of intensive clinical trials in real world scenarios.

But they continue, "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 then there can be no cause-effect relationship. If there is an association, further investigation is required to determine if the relationship is causal. However, a worrying 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."

Effectively to use this method should be to invalidate the findings of it but it never does. As the authors say, "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. They point out that by using this method of risk instead of proving cause, you could easily show a strong association between the wearing of skirts and breast cancer for example. You could also write a convincing paper that shows the crowing of a cock causes the sun to rise because there is an elevated risk that whenever a cock does crow that dawn cannot be too far off. 

The final part of the trickery employed is to publish the research saying "X" is associated with "Y" and then call it an increased risk when no actual causation has been proven. The initial vague possibility is presented as a strong possibility and then reported as an established scientific fact. This is taken even further when it is argued on the media because based on this mis-leading non-scientific trickery, we are then told, "The debate is over." In my own case, so many times on live radio and TV, this stunt has been pulled against me to silence me. 

So now causation is implied due to any kind of association by use of the term risk, or even elevated risk. A researcher notices for example that a large percentage of high blood pressure sufferers are heavy drinkers also. This might indicate an association. Mind you, there may be no association. Stress, for example, can cause blood pressure and perhaps it then leads some people to use alcohol to lessen the stress. But the modern epidemiologist appears not to be overly concerned with confounding details like that, particularly if some anti-drink charity is paying for his research. He knows that they are seeking proof of causation to use as an excuse for more funding. However, blood pressure is a multi-factorial condition or one having potentially many causes often interacting with each other. Epidemiology simply is not designed to prove causation in such an instance. 

That is why the devious researcher turns to his ally 'risk.' Risk is wonderfully flexible and is ever-present in life in all of its forms. If you buy a lottery ticket you have a risk of winning it whereas if you don't buy one there is the actuality that you most certainly will not win. But even if you buy that ticket there is an elevated risk that you still won't win by comparison to the slim chance that you will. So you can regard risk as much like a bookie's odds though without the bookie's canniness. The final piece in the puzzle becomes the compliant PC media. The first you will hear of some new piece of research will be a glaring headline piece in print or on the air. Bad news sells so whatever 'risk' is attributed to anything becomes proven causation by the time it gets to you. It's a bit like a bad weather traffic report. If you hear that road conditions will be icy after dark tonight it is warning you of the risk of a skid. The risk factor can be calculated against the risk of skidding on a non-icy road. But the media report won't say, "If you drive tonight then you will certainly be killed." For smoking though, that is how the 'risk' research is reported.

Thus the "New Epidemiology" debases and devalues science itself. It is the reason you can hear in any given week that something is good for you and the next day it is bad for you. One commentator has called it 'The Prostitutes Science.' This is becaus the biggest confounder in modern epidemiology is who pays the researcher. It even has its own unique term. It's called the "Funding Bias." What it means is that there is a hugely elevated risk that whatever the funder would like the proposed research to discover has a strong possibility of being discovered. Apparently 98 per cent of medical research today is funded by the Pharmaceutical Industry and epidemiology is the favored tool of much of such research. How's that for a glaring conflict of interest then?

So to re-cap, a scientific discipline that was designed to point to the origins of epidemics having a single cause is now being used to determine the cause of illnesses having many potential and inter-acting causes. Unable or unwilling to do the hard work necessary to convert association to causation, a sleight of hand has developed by stating a percentage risk and this is then interpreted as a possible cause. This dubious possible cause is then wrongly reported as a 'probable cause' and over time the lie is repeated often enough until it de-facto becomes an "actual" cause. Underpinning this process is the bias assumed by the funder before the whole charade begins. 

You'd laugh if it wasn't so serious!