Here are links to two maps of the US which show county by county values for:

Air quality
http://www.creativemethods.com/airqu...ted_states.htm

Incidence of Parkinson's
http://www.ncbi.nlm.nih.gov/pmc/arti...395/figure/F2/

What conclusions do you draw?

John

Wow, John. I didn't need any convincing on this connection, but the visuals do have more impact in this case. I hadn't thought to seek this out, but it looks like if we were to overlay the air pollution rates over the PD hot zones, it would be easy to convince others (the power players) that we should be researching this a lot more.

I think somewhere earlier in this thread we mentioned how an area's climate may make a difference. It seems in dryer areas, there is less disease prevalence. Note: Prior to 2006, most studies on air pollution measured mortality and morbidity from cancer and cardiovascular conditions.

I don't know about the big two:pesticides and well-water? The two main pesticide culprits a.k.a. Rotenone and Parquat; I have yet to see studies of chronic or acute exposure to any of these priming the human brain for PD or AD.

From 2006 study acknowledging that air pollution causes brain damage:

Morphometric analysis of the CNS indicated unequivocally that the brain is a critical target for PM exposure and implicated oxidative stress as a predisposing factor that links PM exposure and susceptibility to neurodegeneration.

Brings to mind those seminal books written in the 60's that I should have been required to read but or sought on my own but ignored for whatever reasons....looks like I need to revisit both Future Shock and Silent Spring.

Whatever are we doing to ourselves?

Laura

pesticides and drugs
 

OK SCRATCH THE FIRST POST. I issued myself a skimming ticket.

The second map is something else, east showing much higher than west, even tho the west had some large areas of poor air quality. This seems to reflect industrial pollution.

But my first neuro at my first visit with him said. That's from all the drugs we took in the 60s. He said "we".

Does this also need to be overlaid with population density, and industrial activity, as Paula suggests........

But interesting anyway....

The paper by Willis et al [1] contains, amongst other things, maps at a county-by-county level showing the distribution of the incidence and prevalence of Parkinson's among Medicare beneficiaries.

Incidence: http://www.ncbi.nlm.nih.gov/pmc/arti...395/figure/F2/

Prevalence: http://www.ncbi.nlm.nih.gov/pmc/arti...395/figure/F1/

There is a way to use the difference between these two maps to extract more information. Let me explain how this can be done.

With a disease such as Parkinson's, where there are few, if any, people cured, we would expect the prevalence to be directly related to the incidence. For instance, if the average PwP lives 10 years after diagnosis, the prevalence will be approximately 10 times the incidence.

As far as the maps are concerned, we would expect high incidence counties to be high prevalence counties and low incidence counties to be low prevalence counties. There will always be statistical "noise", so a certain variation is to be expected. There are five levels on the maps (dark green to red). I suggest that any difference, up or down, of at three levels is worth looking at.

The annual incidence figures are for 2002-2005. People don't usually know when they "caught" PD, but most feel that the onset was several years before the first presentation and even longer before the first diagnosis. So, to be a statistic in 2002-2005 we need to look at events in the period from, say, 1995-2005.

What I'd like all of you with knowlege of the US to do is this:
- open both maps, look at areas you are familiar with;
- identify any counties with at least a three step difference (up or down);
- find the population of the county, prioritize the counties with populations above 100000;
- see if you can find any events that may explain the change.

The sort of thing I have in mind are:
- a mine opens or closes;
- fluoride is added or taken away from tap water;
- a pollution event occurs, possibly up-wind;
- a new road is built;
- major lay-offs occur.

Why am I asking you in the US to do this? Simply because you have the best statistics. I don't know of any other paper with the detail of the Willis paper. Also, your county system seems to have just the right granularity for this problem.

Happy hunting. We may just get lucky.

[1] "Geographic and Ethnic Variation in Parkinson Disease: A Population-Based Study of US Medicare Beneficiaries"
Allison Wright Willis, Bradley A. Evanoff, Min Lian, Susan R. Criswell, and Brad A. Racette
Neuroepidemiology. 2010 April; 34(3); 143-151.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2865395/

John

More Data
 

More prevalence data. This time county-by-county data for South Carolina.

See:

"Parkinson's outreach and education training (POET) planning grant, final report, November, 2003"
Forti E., Bergmann K., Salak V., Wall K, Fleming T.
Medical University of South Carolina
http://coa.kumc.edu/gecresource/samp...sonsReport.pdf

The data was collected from "UB-92 billing data".

Does anyone with local knowledge see anything interesting?

John

we listen to doctors
 

who have never had PD - and tell us to use pills they have never sampled, but most doctors wont take chemo!
???
PD is a genetic predisposed heart problem to heavy metal toxicity, ie; mercury
http://www.patientsmedical.com/healt...y/default.aspx
http://www.sunherb.com/dental.htm
http://www.orthomolecular.org/librar...304-p147.shtml

Not so definitive scans......
 

A few short years ago these tests were said to be definitive, and touted as utterly accurate. I am one of the people with a negative scan (done in 2006). There have been others in the same position. Thanks Shetawk for posting these. You do not know how much it means to see this in print.

---------------------

The relative passages from Shetawks posts (my bold) from parkinson.org and PDF:

Recently, in studies that have attempted to diagnose Parkinson’s early in its course, researchers have found that a subset of patients thought to have Parkinson’s disease have turned up with negative PET or SPECT scans. These patients do not seem to develop the progressive symptoms of Parkinson’s disease. These findings are humbling, and they lend credence to the importance of following patients over long periods of time to ensure both accurate diagnosis, and also appropriate treatment.

----------------------------------------

In the study of early Parkinson’s, the overall accuracy of DaTscan was equal to that of the accuracy of a physician’s diagnosis: both were 84 percent.
For people with more advanced PD, the overall accuracy of DaTscan was likewise identical to that of a physician’s diagnosis: both were 98 percent.

The CDC has produced a table showing, state by state, the number of deaths reported to be, at least in part, due to Parkinson's in the US in 2010 [1]. The data is shown in the Appendix. The rate values are per 100,000 of population.

Using this data and the R programming language map package, I've made a map showing the age adjusted PD mortality rates across the US.

Attachment 7512

Parkinson's is well known to be under-reported as a cause of death. But, this is not too much of a problem if we confine ourselves to relative differences, i.e why does state A have 20% higher figures than state B? (Of course, inconsistencies between the reporting conventions of states will affect the usefulness of the numbers.)

It is useful to compare the CDC figures with those in the paper by Willis et al. [2], which has already been mentioned in this thread. The Willis paper reports prevalence and incidence figures, not mortality rates. However, as a rough measure, for a disease like Parkinson's one would expect the annual mortality rate to be similar to the incidence rate. This is because the number of people joining the pool of PwP should be similar to the number leaving it.

I am surprised by the apparent only limited similarity between the two distributions.

Some explanation can be put down to:
- both sets of data are age standardized, but the Willis results are also race standardized.
- the Willis paper is based on Medicare records, which affects the age distribution of its data.
- the Willis paper is on a county by county basis, perhaps a whole state is affected by just a few high density counties.
- for the smaller states the numbers are low, e.g. Delaware reported 57 PD related deaths, suggesting that there could be significant differences from year to year.

Why does this matter? It matters because spatial differences in the rate of Parkinson's offer a good way of identifying environmental factors in the etiology of PD.

References

[1] http://www.cdc.gov/nchs/data/dvs/dea...10_release.pdf

[2] "Geographic and Ethnic Variation in Parkinson Disease: A Population-Based Study of US Medicare Beneficiaries"
Allison Wright Willis, Bradley A. Evanoff, Min Lian, Susan R. Criswell, and Brad A. Racette
Neuroepidemiology. 2010 April; 34(3); 143-151.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2865395/

John

Appendix: CDC data for 2010 PD mortalities
state,number,rate,ageAdjustedRate
Alabama,342,7.2,6.9
Arizona,489,7.7,7.4
Arkansas,206,7.1,6.4
California,2238,6.0,6.5
Colorado,305,6.1,7.2
Connecticut,237,6.6,5.3
Delaware,57,6.3,5.8
District of Columbia,26,4.3,4.6
Florida,1755,9.3,6.7
Georgia,472,4.9,6.3
Idaho,123,7.8,8.3
Illinois,909,7.1,6.9
Indiana,485,7.5,7.2
Iowa,320,10.5,8.1
Kansas,256,9.0,7.9
Kentucky,296,6.8,6.8
Louisiana,265,5.8,6.2
Maine,137,10.3,8.2
Maryland,389,6.7,6.9
Massachusetts,459,7.0,5.9
Michigan,816,8.3,7.4
Minnesota,512,9.7,8.9
Mississippi,174,5.9,6.1
Missouri,477,8.0,7.2
Montana,94,9.5,8.1
Nebraska,183,10.0,8.7
Nevada,149,5.5,6.6
New Hampshire,116,8.8,8.0
New Jersey,645,7.3,6.5
New Mexico,171,8.3,8.3
New York,972,5.0,4.5
North Carolina,636,6.7,6.8
North Dakota,61,9.1,7.0
Ohio,920,8.0,6.9
Oklahoma,249,6.6,6.4
Oregon,356,9.3,8.3
Pennsylvania,1184,9.3,7.1
Rhode Island,96,9.1,7.1
South Carolina,381,8.2,8.3
South Dakota,86,10.6,8.3
Tennessee,435,6.9,6.9
Texas,1492,5.9,7.6
Utah,170,6.2,8.9
Vermont,70,11.2,9.6
Virginia,520,6.5,7.0
Washington,514,7.6,7.9
West Virginia,142,7.7,6.2
Wisconsin,492,8.7,7.4
Wyoming,37,6.6,6.8