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The study of the causes of both good and bad health through the statistical analysis of any type of data. We are particularly interested in geographical, spatial variations in the prevalence of PD.


Finding variations in the prevalence of Parkinson's Disease is of interest for a number of reasons. They may give clues as to:
- the aetiology of the disease;
- the effectiveness of various therapies:
- what environmental toxins are involved in PD.
In short, if we understood the spatial differences in PD's prevalence, we would be much closer to finding a cure.

Why not?

It's not going to be easy. If it were, the disease would have been cured by now. But ...

Why now?

... things are moving to our advantage:
- we have computers that can process data faster and faster;
- we have vastly improved access to data via the internet;
- we have a huge number of people with the equipment, a laptop or PC, to do epidemiological research.
- some government are openning up huge sets of data.

So, it's now possible for someone in Stafford, or Seattle or Singapore, to access billions of pieces of Parkinson's data from Scotland, and to collaborate with somone in Sydney to data mine it.


Data mining South Carolina data suggests environmenal role in PD.

Data mining US county level data suggests an environmental role in the incidence of PD

Maps of inferred relative prevalence of PD in England using prescription data.