Don't throw the epidemiological baby out with the bath water of complexity.

The buses in London used to be mainly double deckers (that is they had two floors with stairs in beween). They had both a driver, who sat in a cab, and a conductor, who went around collecting the fares. It was noticed in the 1940s that drivers had more heart disease than conductors. We are all familiar now with the importance of exercise.

Is the situation with Parkinson's more complcated? Yes, of course it is. (If it were easy, we'd have the answers by now.) But, to our advantage we have computers that can process data billions of times faster than what people could do in the 40s. And, I think, more importantly we have vastly improved access to data via the internet and we have a huge number of people with the equipment, a laptop or PC, to do the research. So, it's now possible for someone in Stafford, or Seattle or Singapore, to access billions of pieces of Parkinson's data and data mine it. The probability that any one one piece of work finds something interesting is, I think, low. But if hundreds of people do it, I'm optimistic that something significant can be found.

Let's look at two of the issues raised in the replies.

Firstly, there's the issue of where a possible cause of the disease isn't included in the present study and goes undetected. An example of salmon eating was given. I have no idea of whether or not salmon eating has a role in the aetiology of PD. But, for the sake of argument, let's suppose it does. Would I detect that using the English data set? No. It would just be noise. But, that's a problem with the data set, not the methodology. In such circumstances, I suspect that an epidemiological study using data from the fishing villages along the coast of Washington, BC and Alaska would give some very interesting results.

Secondly, there's the issue of people moving house after contracting the disease. This tends to reduce the magnitude of the variations from place to place. But it is a matter of noise. Suppose we had two towns A and B, where A was 10:90 (PD, not PD) and B was 20:80 and suppose 20% of the people moved to the other place that would leave us with 12:88 at A and 18:82 at B. Interestingly, we would treat the relative prevalence of 1.61 as the real value when it's really 2. Effects such as this make it more important that we look for spatial variations.

John