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» LymeNet Flash » Questions and Discussion » Medical Questions » AMAZING TEEN WRITES LYME/M.S. PAPER FOR NATIONAL COMPETITION

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Author Topic: AMAZING TEEN WRITES LYME/M.S. PAPER FOR NATIONAL COMPETITION
daystar1952
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Hello all,

About a year ago a high school sophmore contacted me for help with a research paper which was to correlate the areas of lyme with M.S. I knew I wouldn't be of much help so I sent her around to different people. I couldn't believe she was only 15 at the time. She is exceedingly bright and mature for her age....and very sweet besides.

She emailed me the other day and told me that her research paper had been entered in a national competition. She will be hearing from the reviewers at the end of March.

The title of her paper is "A Geostatistical Analysis of Possible Spirochetal Involvement In Multiple Sclerosis and Other Related Diseases."

She sent it to me in an attachment and as I remember we are not to send attachments to the list. She mentioned that she was open to any advice or suggestions and was thrilled to hear that I wanted to have those of you on this list look it over.

If there is anyone here who would like me to send them the attachment, let me know. I'm tellin you, this girl will be an asset to the lyme community and I think we need to encourage young people ....especially ones with this potential.

Margie T

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bettyg
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EDITED: ATTENTION PLEASE
------ ---------------

To all who will read Margie's copy/paste of Megan's paper, please note to go to middle of the thread. This is SINGLE spaced & NO double spaces between paragraphs.

I took this single pieced document and shortened the paragraphs and DOUBLE spaced every one for the entire article. Came out to be 29 pages when I copied it over to my wordperfect software.
ENJOY! You will learn a lot; I did. [bonk]

Margie, this sounds really interesting.

You received this as an attachment, but you should be able to do this:

do a BLOCK COPY of the entire article and then
PASTE it here to this post you originated.

You shouldn't have any problems; I do this all the time. Any problems PM me.

This sounds good enough where we ALL will want to read it. I've got a close onine friend w/severe MS, previous lyme, & it's very aggressive. I'd like to share it with her, and will just copy it from your post here.

Thanks for sharing about this talented 15 year old.

[ 25. February 2006, 11:47 PM: Message edited by: bettyg ]

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daystar1952
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Here we go...we'll see if it works. It's pretty huge with graphs and maps and all


A Geostatistical Analysis of Possible Spirochetal Involvement in Multiple Sclerosis and

Other Related Diseases


Megan M. Blewett 2006
[email protected]



Abstract
A Geostatistical Analysis of Possible Spirochetal Involvement in Multiple Sclerosis and Other Related Diseases

Zoonotic diseases, especially those with insect or arthropod vectors, are recognized public health problems. This class of diseases includes West Nile Virus, Human Granulocytic Ehrlichiosis (HGE), Babesiosis, Rocky Mountain Spotted Fever, and Lyme Disease. This study examines whether Multiple Sclerosis (MS), which is the most common primary neurological disorder of young adults, also belongs in this category. Visual and geostatistical analyses of MS and Lyme reveal striking similarities between the two diseases. Maps displaying each disorder's geographic distribution by county reveal this overlap visually. In addition, the statistical correlation between MS and Lyme deaths (specifically all arthropod-borne disease deaths) is significant at the state-level and highly significant at the county-level. MS incidence is known to vary with latitude; the study's statistical analysis reveals that Lyme Disease follows the same trend. Discussion of possible biological explanations of these geographical and statistical trends is included in this article. Significant correlations also exist with other diseases: on the state level, the correlation between MS and breast cancer is 0.330, and between MS and ALS (Motor Neuron Disease used in this study), the value is 0.618. The control, external accident/injury, did not yield significant correlations. Producing the maps and data required contacting all of the state epidemiologists in the nation for Lyme incidence data. Compiling the data has resulted in one of the most comprehensive Lyme databases available to researchers. The results of the visual, geostatistical, and biochemical analyses suggest common spirochetal involvement in MS and related diseases.
A Geostatistical Analysis of Possible Spirochetal Involvement in Multiple Sclerosis and Other Related Diseases

Introduction

Zoonotic diseases, especially those with insect or arthropod vectors, are well-recognized public health concerns. Such diseases include West Nile Virus, Human Granulocytic Ehrlichiosis (HGE), Babesiosis, Rocky Mountain Spotted Fever, and Lyme Disease. Multiple Sclerosis (MS) is the ``most common primary neurological disorder of young adults'' (Warren, 2001, page 1). The National Multiple Sclerosis Society estimates that 400,000 people in the United States have MS (National Multiple Sclerosis Society, 2005). The National Institute for Neurological Disorders and Stroke (NINDS) reports that the cause of MS is ``linked to an unknown environmental trigger, perhaps a virus (NINDS, 2006a). Although a viral cause of MS is the prevailing view, some researchers believe MS is a zoonotic disease caused by a spirochete and spread by an arthropod vector. This study examines the spirochete hypothesis.
Spirochetal involvement in MS was a hypothesis gaining ground in Europe in the 1930s (Murray, 2005). Unfortunately, most of the research in support of this hypothesis, as well as the researchers themselves, was lost during World War II. A surviving researcher, Gabriel Steiner, published work after World War II that identified a spirochete, Spirochaeta Myelophthora, as the causal agent of MS with an unknown vector (Steiner, 1952; Steiner, 1954). Some of those who worked with Steiner in the United States as well as other researchers hypothesize that MS and Lyme might be either: 1) the same disease; or 2) different diseases caused by two different spirochetes carried by the same arthropod vector (Mattman, 2001; Rubel, 2003; Fritzsche, 2005).
Figure 1. Normalized Count of MS Deaths by County (1998 Deaths Divided by 1990 Census Population)


Figure 2. Normalized Count of Other Specified Arthropod-Borne Diseases (OSABD) Deaths by County (1998 Deaths Divided by 1990 Census Population)

Geostatistical and biochemical analyses reveal many similarities between MS and Lyme. Each is influenced by geography, and MS and Lyme overlap in this geographic distribution. The author began to examine the relationship between MS and Lyme after being struck by the similarity of the distribution apparent in generated distribution maps of both diseases. See Figure 1 and Figure 2. There are also biochemical similarities. NINDS (2006a) defines MS as ``An unpredictable disease of the central nervous system ... in which the body, through its immune system, launches a defensive attack against its own tissues ... the nerve-insulating myelin.'' NINDS (2006b) also recognizes the neurological complications of Lyme, which usually occur in the second stage, and include ``numbness, pain, weakness, Bell's palsy ... visual disturbances, and meningitis symptoms ... decreased concentration, irritability, memory and sleep disorders, and nerve damage in the arms and legs.''
Each of the disorders is characterized by damage to the blood-brain barrier (BBB) endothelium and subsequent increased barrier permeability (Pardridge, 1998). Degradation of the barrier in Lyme patients involves bacterial breakdown of the collagen in the BBB basement membrane. The method of degradation in MS is not known (Russell, 1997), though thickness of the collagen layer could be a factor for prevalence among certain ethnic groups. For example, African-Americans have high levels of collagen and low rates of MS. Both diseases also involve demyelination triggered by what can resemble an autoimmune attack against the myelin sheath. Among MS patients, the mysterious increase in lymphocyte movement across the BBB could be in response to a bacterial invader. Lastly, MS and Lyme disease share an inflammatory response, most likely the work of proinflammatory chemokines and cytokines(Rothwell, 2002). The epidemiological and biochemical similarities suggest, but do not confirm a common bacterial basis for MS and Lyme.
The possibility of a common bacterial basis for both MS and Lyme is examined in this study using geostatistical analysis. Such analysis combines descriptive and inferential statistical techniques with data visualization (cartographics). The results have proven useful in understanding the etiology of many diseases including cholera, plague, malaria, smallpox, AIDS, and Lyme (Ormsby, 2001, Cliff, 2004; Koch, 2005;). The hypothesis to be tested is that MS and Lyme Disease are triggered or influenced by a similar zoonotic spirochetal agent and spread by a tick-like vector. If a common etiology exists, then a geostatistical relationship between Lyme and MS should be observed at either the state-level or the county-level or both. The analysis can be improved by using a control variable (disease) and at least one other condition in which the causal agent or geographic distribution might be similar to that of MS.
The control variable in this study is accident/injury because this condition should be unrelated to a bacterial distribution. The two diseases with a suggested bacterial cause or geographic similarity to MS are Breast Cancer (Cantwell, 1998) and Amyotrophic Lateral Sclerosis (ALS, Lou Gehrig's Disease) (Agency for Toxic Substances and Disease Registry, 2003).
Methods
Comparing disease distributions requires a database of the incidence of the diseases under examination and their associated environmental variables. The data collection process began with a search for an authoritative source of incidence and prevalence data for Lyme, MS, Breast Cancer, ALS, and accidents/injuries. Deaths recorded with the Centers for Disease Control and Prevention (CDC) and other government agencies provide an incidence measure of the given diseases. A useful dataset was found on TheDataWeb, which is an online set of data libraries. The dataset, ``Mortality - Underlying Cause-of-Death - 1998'' (United States Bureau of the Census (Census Bureau), 2005b; CDC, 2005c), was accessed via DataFerret, a data mining tool (Census Bureau, 2005a; CDC, 2005a). The United States Bureau of the Census (Census Bureau) and the Centers Disease Control and Prevention (CDC) make both TheDataWeb and DataFerrett available to the public without charge.
This ``Mortality'' dataset contains geographic, demographic, and cause-of-death variables obtained from the death certificates of people who died in 1998. Geographic variables include: county and state of residence, and county and state population. Cause-of-death-related variables include the underlying-cause-of-death coded using the International Classification of Diseases (ICD) Code (9th Revision).
The coding of death certificate information is standardized across all states. Death certificates are completed and filed at the state-level. (CDC, 2005b). The death certificate information is collected from the states at the federal level by the National Center for Health Statistics (NCHS) and published along with other vital statistics as part of the National Vital Statistics System, ``the oldest and most successful example of inter-governmental data sharing in Public Health and the shared relationships, standards, and procedures form the mechanism by which NCHS collects and disseminates the Nation's official vital statistics.'' (CDC, 2005d, Introduction section). ``The vital statistics general mortality data are a fundamental source of demographic, geographic, and cause-of-death information. This is one of the few sources of comparable health-related data for small geographic areas and a long time period in the United States.'' (Census Bureau, 2005c, National Center for Health Statistics section).
DataFerrett returns information from TheDataWeb in aggregate form only. Upon submitting a DataFerrett query for data the following use restriction statement is displayed:
WARNING! DATA USE RESTRICTIONS. Read Carefully Before Using
The Public Health Service Act (Section 308 (d) ) provides that the data collected by the National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC), may be used only for the purpose of health statistical reporting and analysis. Any effort to determine the identity of any reported case is prohibited by this law. NCHS does all it can to ensure that the identity of data subjects cannot be disclosed. All direct identifiers, as well as any characteristics that might lead to identifications, are omitted from the dataset. Any intentional identification or disclosure of a person or establishment violates the assurances of confidentiality given to the providers of the information. Therefore, users will:
● Use the data in this dataset for statistical reporting and analysis only.
● Make no use of the identity of any person or establishment discovered inadvertently and advise the Director, NCHS, of any such discovery.
● Not link this dataset with individually identifiable data from other NCHS or non-NCHS datasets.
By using the data you signify your agreement to comply with the above-stated statutorily based requirements.
Because DataFerrett queries use the ICD (9th Revision; ICD-9) codes as a selection criteria, the appropriate ICD-9 codes for each disease were determined through review of an online version of this document available from the National Center for Health Statistics (NCHS, 2005). See Table 1 for a list of the ICD-9 codes used as selection criteria. The Disease/Condition DataFerrett Selection Codes were then used to extract the state of residence for those who died in the United States in 1998 from each of the five diseases/conditions of interest. Data was obtained for each of the fifty (50) states and the District of Columbia (total N for the state-level analyses = 51). This data was downloaded into an Excel file.
Added to this Excel file was the population of each state according to both the 1990 Census and the 2000 Census obtained from the Census Bureau American FactFinder, Population Finder website/webtool (Census Bureau, n.d.). The total 1990 population from the Census Bureau and the total 1998 deaths from DataFerrett for each state were used to calculate the incidence variables used in the analyses. See Table 2. The completed Excel file was opened and saved in SPSS (SPSS, 2003), which was used to calculate the descriptive and inferential statistics. The SPSS file was saved as a Dbase IV file and then opened and saved in ArcGIS for the cartographic analyses.
The same general method was used to obtain data at the county level. However, in order to protect the privacy of individuals, DataFerrett does not return data for counties with less than 100,000 people according to the 1990 Census. Instead, all death data for a state from counties with less than 100,000 is lumped into one value.
Wyoming, for example, has no counties with a population of more than 100,000 so the county-level death data for Wyoming is returned as one statewide number.

Disease/ Condition Data Ferrett Selection Code ICD-9 Categories and Code Descriptions

Multiple Sclerosis (MS)
340
Diseases of the Nervous System and Sense Organs (VI: 320-389), Other Disorders of the Central Nervous System (340-349), Multiple Sclerosis (340) - Includes Disseminated or Multiple Sclerosis: Not Otherwise Specified (NOS), Brain Stem, Cord, Generalized


Lyme Disease
088.8
Infectious and Parasitic Diseases (I: 001-139), Rickettsioses and Other Arthropod-Borne Diseases (080-088), Other Arthropod-Borne Diseases (088), Other Specified Arthropod-Borne Diseases (088.8), Lyme Disease (088.81) - includes Erythema Chronicum Migrans, Babesiosis (088.82) - includes Babesiasis, Other (088.89). NOTE: Lyme could not be selected individually because DataFerrett does not allow more detail in selection than 088.8, so analyses were done with this dataset for the category Other Specified Arthropod-Borne Diseases (OSABD) rather than Lyme alone.


Breast Cancer
174.0 - 174.9
Neoplasms (II: 140-239), Malignant Neoplasm of the Female Breast (174) -Includes Nipple and Areola (174.0), Central Portion (174.1), Upper-Inner Quadrant (174.2), Lower-Inner Quadrant (174.3), Upper-Outer Quadrant (174.4), Lower-Outer Quadrant (174.5), Axillary Tail (174.6), Other (174.8), and Breast, Unspecified (174.9)


Amyotrophic Lateral Sclerosis (ALS, Lou Gehrig's Disease)
335.2
Diseases of the Nervous System and Sense Organs (VI: 320-389), Hereditary and Degenerative Diseases of the Central Nervous System (330-337), Anterior Horn Cell Disease (335), Motor Neuron Disease (335.2) - includes Amyotrophic Lateral Sclerosis, Progressive Muscular Atrophy (Pure), and Motor Neuron Disease (Bulbar) (Mixed Type). NOTE: ALS could not be selected individually because ALS does not have its own ICD-9 code. The code for Motor Neuron Disease, which includes ALS was used for the analyses done with this dataset.


External Cause (CONTROL)

E800 - E999
Supplementary Classification of External Causes of Injury and Poisoning (E800 -E999). NOTE: Used as the Control Variable in the analyses.


Table 1. ICD-9 Code Used as the DataFerret Selection Criteria and Reasoning
Variables
Calculation of Variable

MS Death Incidence per 100,000 Live (1990) Number of deaths from MS in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.
MS Death Incidence per 100,000 Deaths (1998) Number of deaths from MS in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.
OSABD Death Incidence per 100,000 Live (1990) Number of deaths from OSABD in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.
OSABD Death Incidence per 100,000 Deaths (1998) Number of deaths from OSABD in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.
1998 Lyme Incidence per 100,000 Live (1990) Number of new Lyme cases reported by State Epidemiologists to the CDC for 1998 for that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.
1992-1998 Lyme Incidence per 100,000 Live (1990) Total of the number of new Lyme cases reported by State Epidemiologists to the CDC for each of the years between 1992 and 1998 for that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.
Breast Cancer Death Incidence per 100,000 Live (1990) Number of deaths from Breast Cancer in1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.
Breast Cancer Death Incidence per 100,000 Deaths (1998) Number of deaths from Breast Cancer in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.
Motor Neuron Death Incidence per 100,000 Live (1990) Number of deaths from Motor Neuron Disease in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit
Motor Neuron Death Incidence per 100,000 Deaths (1998) Number of deaths from Breast Cancer in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.
External Cause Death Incidence per 100,000 Live (1990) Number of deaths from External Causes in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit
External Cause Death Incidence per 100,000 Deaths (1998) Number of deaths from External Causes in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.

Table 2. Calculation of Variables Used in the Dataset of Variables for Data Analysis
Delaware's three counties each have a population over 100,000 so county-level data is returned for all three Delaware counties. New Jersey has twenty-one counties, but three of these counties have a population less than 100,000. For New Jersey, data is returned for each of eighteen individual counties and then one number is returned for the three counties (combined) with a population of less than 100,000.
There are 3141 counties in the United States, but DataFerrett returns data on 504, which includes the combined values for a state's less-than-100,000 counties. At the county-level, the population data was obtained from Census data available through the University of Virginia (n.d.). County-level analyses were also done using only those states generally considered to have a high Lyme incidence (Lyme-State). These 123 Lyme-State counties, which include those counties lumped together because of a less-than-100,000 population, are in the following ten states: Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, and Vermont.
All statistical calculations were done using SPSS. Counts of disease deaths provided by the CDC were normalized by the 1990 Census population information, yielding number of deaths due to a certain disease per 100,000 people in that state or county. See Table 2. But normalizing disease deaths by the number of living people in a state or county produced the confounding factor of that geographic unit's demographics and age. So a new measurement was introduced: the number of deaths from each disease was divided over the total deaths of each county or state (incidence of death due to a specific disease per 100,000 deaths in that geographic unit). See Table 2. Another confounding factor was the exclusion of counties with fewer than 100,000 residents due to CDC privacy policy. To accommodate for this, the total deaths from all of these smaller counties was smeared proportionally across each county included in the set. This set of all the counties with fewer than 100,000 people was labeled a ``super-county''. The analysis could use these blocks in combination or independently.
To this data, in both the state and county files, was added the number of new Lyme cases reported each year from 1992-1998, centroid latitude, centroid longitude, and population elevation (the elevation of the county seat or the nearest population center to the county seat for which there is elevation data). Centroid latitude and longitude were averaged over all counties in a state to calculate the state value. The same method was used to calculate each state's population elevation. Centroid latitude, centroid longitude, and most population elevation information were obtained from the United States Geological Survey (USGS, n.d.). The Lyme case data was added because the death data from DataFerrett includes more than Lyme (See Table 1). The DataFerrett category that includes Lyme deaths is ``Other Specified Arthropod Borne Diseases'' in ICD-9. This category variable is named OSABD in this study.
The number of Lyme cases in each state for the years 1992-1998 is available from CDC publications (CDC, 2002). The number of Lyme cases per year by county is not, however, available from the CDC. Although the CDC publishes some multi-year cartographic material by county, the CDC does not report county-level, annual numerical data for a state to the public. County-level Lyme incidence data is only available to the public by contacting each state's department of health, specifically, the state epidemiologist. In this study, Lyme data available by county was subsequently compiled to match the super-counties data available for DataFerrett death data.
The process of obtaining Lyme incidence data by county for the years 1992 through and including 1998 was labor-intensive. Each state's Department of Health website was visited to see if the needed Lyme data was available on the website. If the data was not available, that state's epidemiologist was emailed using contact information from the Council of State and Territorial Epidemiologists (n.d.) website provided by the CDC. Most epidemiologists contacted via email responded and provided the necessary data. All of these sources were recorded and the data compiled and added to the database. As of this writing, this appears to be the most comprehensive database of Lyme in existence.
Results
Descriptive statistics for the variables in each of the three basic datasets can be found in Table 3, Table 4, and Table 5. As many statistical tests assume that the data are normally distributed, each variable's skewness and kurtosis values and standard errors were examined. A normally distributed variable has a value of 0 for both skewness (a measure of symmetry) and kurtosis (a measure of clustering around a central point). If the ratio of the skewness value to its standard error is between -2 and +2, then the distribution is symmetrical (normal). If the ratio of the kurtosis value to its standard error is between -2 and +2, then the data are normally distributed. (SPSS, 2003; Norusis, 2003).
Few of the variables are normally distributed. In the State-Level variables, only MS Death Incidence per 100,000 Live (1990), MS Death Incidence per 100,000 Deaths (1998), Motor Neuron Death Incidence per 100,000 Live (1990), Motor Neuron Death Incidence per 100,000 Deaths (1998), and External Cause Death Incidence per 100,000 Live (1990) are normally distributed. In the Lyme-State County Level (Population >= 100,000) variables, only MS Death Incidence per 100,000 Live (1990) and Breast Cancer Death Incidence per 100,000 Deaths (1998) are normally distributed.
The next step in the analysis was a correlation analysis. Calculating a Pearson correlation coefficient (r) is appropriate for variables that are normally distributed. (SPSS, 2003, page 379). Calculating a Kendall's tau-b or Spearman's rho is appropriate when the data are not normally distributed. Because all three of these correlation analyses assume a linear relationship between the variables, a scatterplot graph was constructed for each pair of variables to be analyzed. Each scatterplot was linear so a Pearson's, Kendall's, or Spearman's coefficient was calculated as appropriate for pairs of variables in each of the three datasets. The results can be seen in Table 6, Table 7, and Table 8.
Multiple regression was also used to find the model that would best predict the MS Death Incidence per 100,000 Deaths. All variables contained in the dataset were entered into the regression analysis using the stepwise feature. All variable values were converted to z-scores for use in the regression analysis. These results can be seen in Table 9. Lastly, cartographic analyses were completed. These can be seen in Figure 1, Figure 2, and Figure 3. They show the normalized distribution of MS Deaths, OSABD Deaths, and External Causes Deaths, respectively.



Dataset of State-Level Disease and Geographic Variables N Min Max Mean Std. Dev. Skewness Kurtosis
Value Std. Err. Value Std. Err.
MS Death Incidence per 100,000 Live (1990) 51 0.1 2.0 1.1 0.4 0.2 0.3 0.5 0.7
MS Death Incidence per 100,000 Deaths (1998) 51 12.4 219.6 112.8 43.7 0.3 0.3 -0.1 0.7
OSABD Death Incidence per 100,000 Live (1990) 51 1.5 7.2 3.6 1.6 0.8 0.3 -0.5 0.7
OSABD Death Incidence per 100,000 Deaths (1998) 51 159.0 803.9 385.0 166.6 0.9 0.3 0.1 0.7
1998 Lyme Incidence per 100,000 Live (1990) 51 0.0 104.5 6.7 18.6 4.2 0.3 18.9 0.7
1992-1998 Lyme Incidence per 100,000 Live (1990) 51 0.0 472.2 33.6 83.7 3.9 0.3 16.8 0.7
Breast Cancer Death Incidence per 100,000 Live (1990) 51 8.9 22.1 16.8 2.3 -0.6 0.3 1.9 0.7
Breast Cancer Death Incidence per 100,000 Deaths (1998) 51 1377.7 2213.4 1772.1 186.6 0.3 0.3 2.1 0.7
Motor Neuron Death Incidence per 100,000 Live (1990) 51 0.7 2.7 1.8 0.5 0.0 0.3 -0.4 0.7
Motor Neuron Death Incidence per 100,000 Deaths (1998) 51 98.9 303.1 187.1 45.1 0.2 0.3 -0.0 0.7
External Cause Death Incidence per 100,000 Live (1990) 51 39.8 109.8 66.1 15.8 0.4 0.3 0.1 0.7
External Cause Death Incidence per 100,000 Deaths (1998) 51 4227.4 16802.8 7067.0 2068.1 2.3 0.3 9.0 0.7
Population Elevation (feet) 51 18.0 6305.4 1337.7 1602.0 1.8 0.3 2.3 0.7
Centroid Latitude 51 21.0 60.3 39.5 5.9 0.1 0.3 3.2 0.7
Centroid Longitude 51 -157.3 -69.5 -93.4 19.0 -1.3 0.3 2.1 0.7

Table 3. Descriptive Statistics for the Dataset of State-Level Disease and Geographic Variables

Dataset of County-Level (Population >=100,000) Disease and Geographic Variables N Min Max Mean Std. Dev. Skewness Kurtosis
Value Std. Err. Value Std. Err.
MS Death Incidence per 100,000 Live (1990) 504 0.0 4.3 1.0 0.7 0.9 0.1 1.5 0.2
MS Death Incidence per 100,000 Deaths (1998) 504 0.0 523.6 112.2 81.4 0.9 0.1 1.9 0.2
OSABD Death Incidence per 100,000 Live (1990) 504 0.0 14.9 3.3 2.1 1.5 0.1 4.0 0.2
OSABD Death Incidence per 100,000 Deaths (1998) 504 0.0 1905.0 354.2 221.5 1.6 0.1 5.2 0.2
1998 Lyme Incidence per 100,000 Live (1990) 504 0.0 485.8 10.3 43.2 7.3 0.1 61.0 0.2
1992-1998 Lyme Incidence per 100,000 Live (1990) 504 0.0 2743.6 51.1 213.4 7.6 0.1 71.0 0.2
Breast Cancer Death Incidence per 100,000 Live (1990) 504 1.8 35.1 16.7 4.1 0.5 0.1 1.5 0.2
Breast Cancer Death Incidence per 100,000 Deaths (1998) 504 221.0 3081.5 1815.4 368.6 0.0 0.1 0.8 0.2
Motor Neuron Death Incidence per 100,000 Live (1990) 504 0.0 6.4 1.8 1.1 0.7 0.1 0.9 0.2
Motor Neuron Death Incidence per 100,000 Deaths (1998) 504 0.0 661.0 193.5 112.8 0.8 0.1 1.2 0.2
External Cause Death Incidence per 100,000 Live (1990) 504 25.0 140.2 59.4 17.9 1.0 0.1 1.8 0.2
External Cause Death Incidence per 100,000 Deaths (1998) 504 2970.3 18308.1 6477.6 1831.9 1.3 0.1 4.2 0.2
Population Elevation (feet) 504 -40.0 6485.9 753.6 1090.8 3.0 0.1 9.6 0.2
Centroid Latitude 504 19.5 61.2 38.3 5.2 -0.38 0.1 1.2 0.2
Centroid Longitude 504 -158.0 -68.7 -89.6 16.0 -1.3 0.1 1.7 0.2

Table 4. Descriptive Statistics for the Dataset of County-Level (Population >= 100,000) Disease and Geographic Variables
Dataset of Lyme State County-Level (Population >=100,000) Disease and Geographic Variables N Min Max Mean Std. Dev. Skewness Kurtosis
Value Std. Err. Value Std. Err.
MS Death Incidence per 100,000 Live (1990) 123 0.0 2.8 1.0 0.6 0.4 0.2 0.2 0.4
MS Death Incidence per 100,000 Deaths (1998) 123 0.0 412.1 107.2 71.5 0.8 0.2 2.0 0.4
OSABD Death Incidence per 100,000 Live (1990) 123 0.0 7.2 2.6 1.3 1.0 0.2 1.6 0.4
OSABD Death Incidence per 100,000 Deaths (1998) 123 0.0 815.3 275.0 139.8 0.9 0.2 1.4 0.4
1998 Lyme Incidence per 100,000 Live (1990) 123 0.0 485.8 35.4 73.3 3.7 0.2 16.0 0.4
1992-1998 Lyme Incidence per 100,000 Live (1990) 123 2.4 2743.6 176.6 378.7 4.0 0.2 19.9 0.4
Breast Cancer Death Incidence per 100,000 Live (1990) 123 9.9 35.1 18.3 3.7 0.9 0.2 3.1 0.4
Breast Cancer Death Incidence per 100,000 Deaths (1998) 123 1061.0 3081.5 1957.8 339.1 0.2 0.2 0.5 0.4
Motor Neuron Death Incidence per 100,000 Live (1990) 123 0.0 4.8 1.8 1.1 0.7 0.2 0.3 0.4
Motor Neuron Death Incidence per 100,000 Deaths (1998) 123 0.0 632.2 198.4 120.9 1.1 0.2 1.8 0.4
External Cause Death Incidence per 100,000 Live (1990) 123 25.0 118.5 47.7 12.6 1.6 0.2 7.2 0.4
External Cause Death Incidence per 100,000 Deaths (1998) 123 2970.3 10056.5 5077.8 1126.6 1.6 0.2 5.5 0.4
Population Elevation (feet) 123 9.0 2140.0 341.7 355.9 1.9 0.2 5.0 0.4
Centroid Latitude 123 38.5 45.2 41.3 1.5 0.5 0.2 -0.3 0.4
Centroid Longitude 123 -80.5 -68.7 -74.9 2.6 -0.1 0.2 -0.3 0.4

Table 5. Descriptive Statistics for the Dataset of Lyme State County-Level (Population >= 100,000) Disease and Geographic Variables


Statistically Significant Correlations in the Dataset of State-Level Disease and Geographic Variables (MS Variables and Other Variables) N MS Death Incidence per 100,000 Live (1990) MS Death Incidence per 100,000 Deaths (1998)
OSABD Death Incidence per 100,000 Live (1990) 51 Kendall's tau_b: 0.213*
Sig. (2-tailed): 0.028
Spearman's rho: 0.293*
Sig. (2-tailed): 0.037 No statistically significant correlation.
Breast Cancer Death Incidence per 100,000 Deaths (1998) 51 No statistically significant correlation Kendall's tau_b: 0.222*
Sig. (2-tailed): 0.022
Spearman's rho: 0.330*
Sig. (2-tailed): 0.018
Motor Neuron Death Incidence per 100,000 Live (1990) 51 Pearson: 0.569**
Sig. (2-tailed): 0.000 Pearson: 0.413**
Sig. (2-tailed): 0.003
Motor Neuron Death Incidence per 100,000 Deaths (1998) 51 Pearson: 0.628**
Sig. (2-tailed): 0.000 Pearson: 0.618**
Sig. (2-tailed): 0.000
Population Elevation (feet) 51 Kendall's tau_b: 0.269**
Sig. (2-tailed): 0.005
Spearman's rho: 0.404**
Sig. (2-tailed): 0.003 Kendall's tau_b: 0.286**
Sig. (2-tailed): 0.003
Spearman's rho: 0.401**
Sig. (2-tailed): 0.004
Centroid Latitude 51 Kendall's tau_b: 0.522**
Sig. (2-tailed): 0.000
Spearman's rho: 0.669**
Sig. (2-tailed): 0.000 Kendall's tau_b: 0.529**
Sig. (2-tailed): 0.000
Spearman's rho: 0.692**
Sig. (2-tailed): 0.000

Table 6. Statistically Significant Correlations in the Dataset of State-Level Disease and Geographic Variables (MS Variables and Other Variables)


Discussion
The results of the statistical analyses support geographically the proposed connection between Multiple Sclerosis, Lyme, and related diseases. The cartographic display in Figure 1 and Figure 2 show a clear similarity between MS and OSABD, which includes Lyme. Figure 3, which displays the control variable, is very different. The correlations and regression analysis also show a clear geographic co-occurrence of MS and Lyme. Because there is no such relationship with the control variable, External Deaths, a common cause for MS and Lyme is suggested. The strong association of MS with Motor Neuron Disease (ALS) and the weaker, but significant, association with

Statistically Significant Correlations in the Dataset of County-Level (Population >=100,000) Disease and Geographic Variables (MS Variables and Other Variables) N MS Death Incidence per 100,000 Live (1990) MS Death Incidence per 100,000 Deaths (1998)
OSABD Death Incidence per 100,000 Live (1990) 504 Kendall's tau_b: 0.119**
Sig. (2-tailed): 0.000
Spearman's rho: 0.174**
Sig. (2-tailed): 0.000 Kendall's tau_b: 0.068*
Sig. (2-tailed): 0.023
Spearman's rho: 0.101**
Sig. (2-tailed): 0.024
OSABD Death Incidence per 100,000 Deaths (1998) 504 Kendall's tau_b: 0.064*
Sig. (2-tailed): 0.035
Spearman's rho: 0.094*
Sig. (2-tailed): .0360 Kendall's tau_b: 0.079**
Sig. (2-tailed): 0.009
Spearman's rho: 0.114**
Sig. (2-tailed): 0.010
Breast Cancer Death Incidence per 100,000 Live (1990) 504 Kendall's tau_b: 0.144**
Sig. (2-tailed): 0.000
Spearman's rho: 0.209**
Sig. (2-tailed): 0.000 No statistically significant correlation.
Breast Cancer Death Incidence per 100,000 Deaths (1998) 504 No statistically significant correlation. Kendall's tau_b: 0.099**
Sig. (2-tailed): 0.001
Spearman's rho: 0.146**
Sig. (2-tailed): 0.001
Motor Neuron Death Incidence per 100,000 Live (1990) 504 Kendall's tau_b: 0.134**
Sig. (2-tailed): 0.000
Spearman's rho: 0.183**
Sig. (2-tailed): 0.000 Kendall's tau_b: 0.076*
Sig. (2-tailed): 0.011
Spearman's rho: 0.106*
Sig. (2-tailed): 0.017
Motor Neuron Death Incidence per 100,000 Deaths (1998) 504 Kendall's tau_b: 0.091**
Sig. (2-tailed): 0.002
Spearman's rho: 0.125**
Sig. (2-tailed): 0.005 Kendall's tau_b: 0.114**
Sig. (2-tailed): 0.000
Spearman's rho: 0.155**
Sig. (2-tailed): 0.000
External Cause Death Incidence per 100,000 Live (1990) 504 No statistically significant correlation. Kendall's tau_b: -0.073*
Sig. (2-tailed): 0.016
Spearman's rho: -0.108*
Sig. (2-tailed): 0.015
External Cause Death Incidence per 100,000 Deaths (1998) 504 Kendall's tau_b: -0.079**
Sig. (2-tailed): 0.009
Spearman's rho: -0.117**
Sig. (2-tailed): 0.008 No statistically significant correlation.
Centroid Latitude 504 Kendall's tau_b: 0.173**
Sig. (2-tailed): 0.000
Spearman's rho: 0.249**
Sig. (2-tailed): 0.000 Kendall's tau_b: 0.203**
Sig. (2-tailed): 0.000
Spearman's rho: 0.296**
Sig. (2-tailed): 0.000

Table 7. Statistically Significant Correlations in the Dataset of County-Level (Population >=100,000) Disease and Geographic Variables (MS Variables and Other Variables)


Statistically Significant Correlations in the Basic Set of Dataset of Lyme State County-Level (Population >=100,000) Disease and Geographic Variables (MS Variables and Other Variables) N MS Death Incidence per 100,000 Live (1990) MS Death Incidence per 100,000 Deaths (1998)
Breast Cancer Death Incidence per 100,000 Deaths (1998) 123 No statistically significant correlation. Kendall's tau_b: 0.152*
Sig. (2-tailed): 0.014
Spearman's rho: 0.221*
Sig. (2-tailed): 0.014
External Cause Death Incidence per 100,000 Live (1990) 123 No statistically significant correlation. Kendall's tau_b: -0.152*
Sig. (2-tailed): 0.013
Spearman's rho: -0.221*
Sig. (2-tailed): 0.014
Centroid Latitude 123 Kendall's tau_b: 0.136*
Sig. (2-tailed): 0.027
Spearman's rho: 0.199*
Sig. (2-tailed): 0.027 Kendall's tau_b: 0.134*
Sig. (2-tailed): 0.029
Spearman's rho: 0.196*
Sig. (2-tailed): 0.029
Centroid Longitude 123 Kendall's tau_b: 0.129*
Sig. (2-tailed): 0.035
Spearman's rho: 0.192*
Sig. (2-tailed): 0.033 Kendall's tau_b: 0.149*
Sig. (2-tailed): 0.016
Spearman's rho: 0.226*
Sig. (2-tailed): 0.012

Table 8. Statistically Significant Correlations in the Dataset of Basic Set of Lyme State County-Level (Population >=100,000) Disease and Geographic Variables (MS Variables and Other Variables)


Dependent Variable Independent Variables R Square
State-Level (N=51) Z-Score of MS Death Incidence per 100,000 Deaths (1998) Constant = -4.539E-16
Z-Score of Motor Neuron Death Incidence per 100,000 Deaths (1998) (B = .354)
Z-Score of Centroid Latitude (B = .378)
Z-Score of OSABD Death Incidence per 100,000 Deaths (1998) (B = .259) .554
County-Level (N=504) Z-Score of MS Death Incidence per 100,000 Deaths (1998) Constant = .196
Z-Score of Centroid Latitude (B = .406)
Z-Score of OSABD Death Incidence per 100,000 Deaths (1998) (B = .200)
Z-Score of Breast Cancer Death Incidence per 100,000 Deaths (1998) (B = .099) .109
Lyme State County-Level (N=123) Z-Score of MS Death Incidence per 100,000 Deaths (1998) Constant = -.867
Z-Score 1992-1998 Lyme Incidence per 100,000 Live (1990) (B = .176)
Z-Score of Breast Cancer Death Incidence per 100,000 Deaths (1998) (B = .210)
Z-Score of Centroid Latitude (B = 1.051) .134

Table 9. Multiple Regression Analysis of Z-Score of MS Death Incidence per 100,000 Deaths (1998) Variable at the State-Level, County-Level, and Lyme State County-Level: All Basic Set Variables Included in the Stepwise Analysis

Figure 3. Normalized Count of External Causes of Death by County (1998 Deaths Divided by 1990 Census Population)


Breast Cancer, also suggest a possible common environmental, spirochetal, mechanism for these diseases. The well-known relationship between latitude and MS (Warren, 1998) is also seen in these analyses. This relationship is also statistically significant for both Breast Cancer and Motor Neuron Disease at the state and county level as well as Lyme at the county level.
The overlap between MS and Lyme is not solely geographic; the results of the statistical analyses can be explained using biochemical principles as well. Both diseases involve vascular inflammation within the Central Nervous System (CNS) caused in part by inflammatory cytokines and chemokines (Pardridge, 1998). Tissue
plasminogen activator (tPA) regenerates plasmin and allows penetration not only by
bacteria but by other invaders as well (Pardridge, 1998). Borrelia Burgdorferi, the causative bacterial agent of Lyme Disease, uses tPA in order to degrade the collagen layer of the Blood-brain barrier (BBB) and enter the CNS. Likewise, tPA is found in the MS BBB, though its role is currently unknown (Pardridge, 1998).
Once the unknown invader moves within the CNS, one of the first responses to attack is the clustering of macrophages around the sclerotic plaques of MS. Macrophages have two main functions: to digest dead cell material and to digest bacteria by phagocytosis (Guyton, 1997). While the macrophages might be serving to break down remnants of myelin already attacked by the unknown antigen, the macrophages' secretion of Nitrogen Monoxide (NO) seems to suggest that some bacteria is also present. NO plays a number of different roles in disease, both positive and negative; it may induce axonal degeneration or vascular dilation, serve as a signaling molecule between neurons, affect memory and thought processes of the brain, or kill bacteria (Guyton, 1997). Parallels exist not only in Lyme Disease but within other diseases as well. One example is Leishmania, a parasitic disease which affects the body's internal organs and immune system. The macrophages involved secrete NO to kill the antigen, a protozoan (CDC, 2004). A similar mechanism against a spirochetal invader could be at work in MS.
Lyme resembles MS more and more as it progresses within the body. In its most developed stages, it mimics an autoimmune attack against the myelin sheath, which is what most researchers believe MS to be (Filley, 2001). But the autoimmune theory does not explain very well the relapse-remitting progression common in both MS and Lyme. If in fact the T cells and the body's immune cells are primed not to attack an unknown invader but to attack the Myelin Basic Protein (MBP) or some other feature of the fatty sheath surrounding the axons, then one would not expect the disease to remit when there is still myelin left to be digested.
The presence of spirochetes seems to provide a reasonable solution. Lyme follows a relapse-remitting progression due to the many different forms that spirochetes such as Borrelia Burgdorferi are known to take. When the environment is positive for the spirochetal activity, the bacteria remain in a fully elongated form (about 5-20 μm in length), but in the presence of antibiotics many spirochetes defensively curl up into a granular form (about .3-.5μm) (Mattman, 2001). While in the granular form, the spirochetes are virtually undetectable even by electron microscopy, and the disease appears to be latent for some time. This latency period, though, is perhaps the most deleterious stage of disease. While in their highly minimized forms, the spirochetes are able to traverse many of the body's pores and enter into cells and organs (Saier, 2001). When no longer threatened, they expand again into their elongated form.
Spirochetes thrive upon steroids, yet most MS medications use steroids to reduce neural inflammation (Russell, 1997). The steroids could be playing additional roles if MS is in fact influenced by spirochetes. Although spirochetes thrive in the presence of steroids, the steroids could bring about the bacteria's destruction. Acting as a sort of bait, often steroids cause spirochetes expand into their elongated forms, though in this form the bacteria are much more susceptible to T-cell attack (Mattman, 2001). This could explain the success of steroids as a medication and provide some insight for developing more permanent solutions.
Spirochetes may also act as a gateway for certain types of cancer. Because the spirochetes are so amorphous, they can mimic the body's own cells. Looking life self-material, the bacteria manage to fuse with the cell walls and from there eventually control the activities of the cell, often resulting in cancer (Mattman, 2001). The statistical correlations agree with this; the correlation between MS and breast cancer is significant.
One testament to the connection between MS and Lyme is the difficulty that doctors face in distinguishing between the two when making a diagnosis. In certain cases, patients are misdiagnosed several times. Both diseases can produce MRI's marked by sclerotic plaques, and both manifest similar symptoms such as memory lapses, fatigue, and joint pain (Warren, 2001). The age of onset of MS is typically between 35 and 40 years of age. Likewise, one of the peak age groups to acquire Lyme Disease falls in this range. Epidemiological studies tracking the movement of MS patients from areas of higher MS incidence to lower MS incidence have revealed that the unknown trigger for MS is most likely encountered around twelve years of age (Warren, 2001). Similarly, the majority of Lyme patients acquire the disease when they are in this stage of adolescence. This suggests that MS might develop from a secondary spirochete bite, though other factors such as stress and natural aging could also trigger its onset.
The study, though, is not free of confounding factors. In studies of this nature, one must worry about spurious correlations. The control (external accident/injury) seems reasonable. Secondly, the geographical distribution of MS and Lyme deaths represents not only the presence of an etiological agent, but social trends as well. Often people diagnosed with chronic illnesses move to other, more hospitable regions of the United States like Florida or California, or to regions with better healthcare such as states along the East Coast, particularly for MS. The use of death rates rather than diagnosis rates provided more definitive information, though it introduced the variable of healthcare.
States which have higher rates of diagnosis, in fact, sometimes display lower death rates, because, with experience, doctors in those areas often are more familiar with treating the disease. Excluding counties with less than 100,000 residents also presented confounding factors. Because Lyme is known to be transmitted by ticks in wooded areas, much of the Lyme incidence occurs in more rural counties in which the boundary between people and nature is less well defined. As mentioned previously, for reasons of confidentiality, for each state the CDC released only total deaths of all the counties with less than 100,000 people. This introduced a smearing effect in which some vital Lyme information may have been washed out. Nonetheless, sufficient similarities exist in this study to suggest, but not confirm, a common spirochetal basis for MS and Lyme.
References

Agency for Toxic Substances and Disease Registry (ATSDR). (2003, last updated May). Multiple Sclerosis and Amyotrophic Lateral Sclerosis-Related Projects: Ongoing and Completed Projects, Health Investigations Branch, Division of Health Studies. Access at: http://www.atsdr.cdc.gov/DHS/MS_Fact_Sheet.html

Cantwell, A. (1998). Do killer microbes cause Breast Cancer? New Dawn: A Journal of Alternative News and Information, 48 (electronic copy). Available at: http://www.newdawnmagazine.com/Articles/Do%20Killer%20Microbes%20Cause%20Breast%20Cancer.html

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). (2005a). DataWarehouse, Accessing NCHS Data in DataFerrett. Access: http://www.cdc.gov/nchs/datawh/ferret/ferret.htm

Centers for Disease Control and Prevention (CDC). (2002). Lyme Disease - United States, 2000. Morbidity and Mortality Weekly Report (MMWR), 51(02), 29-31.

Centers for Disease Control and Prevention (CDC), Division of Parasitic Diseases (2004, last updated April). Parasitic Disease Information: Leishmania Infection Factsheet. Access:
http://www.cdc.gov/ncidod/dpd/parasites/leishmania/factsht_leishmania.htm

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). (2005b). Mortality Data from the National Vital Statistics System. Access: http://www.cdc.gov/nchs/deaths.htm

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), Publications and Information Products. (2005c). Mortality Data and Underlying Cause of Death Public-Use Files. Access: http://www.cdc.gov/nchs/products/elec_prods/subject/mortucd.htm

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). (2005d). National Vital Statistics System. Access: http://www.cdc.gov/nchs/nvss.htm

Cliff, A., Haggett, P, & Smallman-Raynor, M. (2004). World Atlas of Epidemic Diseases. New York: Oxford University Press.

Council of State Epidemiologists. (n.d.). Directory. Access: http://www.cste.org/members/state_and_territorial_epi.asp and http://www.cste.org

Filley, C.M. (2001). The Behavioral Neurology of White Matter. New York: Oxford University Press.

Fritzsche, M. (2005). Chronic lyme borreliosis at the root of multiple sclerosis - is a cure with antibiotics attainable? Medical Hypotheses, 64(3), 438-448.

Guyton, A.C. & Hall, J.E. (1997) Human Physiology and Mechanisms of Disease (6th Edition). Philadelphia: W.B. Saunders

Koch, T. (2005). Cartographics of Disease: Maps, Mapping, and Medicine. California: ESRI Press.

Mattman, L.H. (2001). Cell Wall Deficient Forms: Stealth Pathogens (Third Edition). New York: CRC Press.

McKinnell, R.G., Parchment, R.E., Perantoni, A.O., & Pierce, G.B. (2003). The Biological Basis of Cancer. Cambridge: Cambridge University Press.

Murray, T.J. (2005). Multiple Sclerosis: The History of a Disease. New York: Demos Medical Publishing.

National Center for Health Statistics. (2005, last updated August). Mortality Data from the National Vital Statistics System, International Classification of Diseases, Ninth Revision (ICD-9), Volume I. Accessed at ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD-9/

National Multiple Sclerosis Society. (2005, last updated October). Epidemiology. Retrieved from http://www.nationalmssociety.org/Sourcebook-Epidemiology.asp

National Institute of Neurological Disorders and Stroke (NINDS). (2006a, last updated January). NINDS Multiple Sclerosis Information Page. Retrieved from http://www.ninds.nih.gov/disorders/multiple_sclerosis/multiple_sclerosis.htm

National Institute of Neurological Disorders and Stroke (NINDS). (2006b, last updated January). NINDS Neurological Complications of Lyme Disease Information Page. Retrieved from http://www.ninds.nih.gov/disorders/lyme/lyme.htm

Norusis, M.J. (2003). SPSS 12.0 Statistical Procedures Companion. Upper Saddle River, New Jersey: Prentice Hall.

Ormsby, T., Napoleon, E., Burke, R., Groessl, C., & Feaster, L. (2001). Getting to Know ArcGIS Desktop: Basics of ArcView, ArcEditor, and ArcInfo. California: ESRI Press.

Pardridge, W.M. (Ed.). (1998). Introduction to the Blood-Brain Barrier: Methodology, Biology, and Pathology. Cambridge: Cambridge University Press.

Rothwell, N. & Loddick, S. (Ed.). (2002). Immune and Inflammatory Responses in the Nervous System (Second Edition). Oxford: Oxford University Press.

Rubel, J. (Ed.). (2003). Lyme disease survival in adverse conditions: the strategy of morphological variation in Borrelia burgdorferi & other spirochetes 1900-2001 (electronic). Lyme Info: Cystic Form of Bb & Other Spirochetes: Advanced. Accessed at http://www.lymeinfo.net/medical/LDAdverseConditions.pdf

Russell, W.C. (Ed.). (1997). Molecular Biology of Multiple Sclerosis. New York: John Wiley &Sons.

Saier, M.H. & Garcia-Lara, J. (2001). The Spirochetes: Molecular and Cellular Biology. Wiltshire: United Kingon: Horizon Press.

SPSS. (2003). SPSS Base 12.0 User's Guide. Chicago, Illinois: Author.
Steiner, G. (1952). Acute plaques in multiple sclerosis, their pathogenic significance and the role of spirochetes as etiological factor. Journal of Neuropathology and Experimental Neurology, 11(4), 343-372

Steiner, G. (1954). Morphology of spirochaeta myelophthora in multiple sclerosis. Journal of Neuropathology, 13, 221-229.

United States Census Bureau. (n.d.). American FactFinder, Population Finder (Data WebTool). Access: http://factfinder.census.gov/servlet/SAFFPopulation?_submenuId=population_0&_sse=on

United States Census Bureau and Centers for Disease Control and Prevention (CDC). (2005a). DataFerrett: For TheDataWeb. Access: http://dataferrett.census.gov/index.html.

United States Census Bureau and Centers for Disease Control and Prevention (CDC). (2005b). National Center for Health Statistics, Mortality - Underlying Cause of Death, 1998 [Data WebTool]. Available from TheDataWeb website, http://www.thedataweb.org/index.html.

United States Census Bureau and Centers for Disease Control and Prevention (CDC). (2005c). TheDataWeb: Description of Datasets Available Using DataFerrett. Access: http://www.thedataweb.org/datasets.html

United States Geological Survey (n.d.). Geographic Names Information System (Data WebTool). Access: http://geonames.usgs.gov/fips55/fips55down.html

University of Virginia. (n.d.). University of Virginia Library Geostat Center: Collections for the 1990 and 2000 Populations (Data WebTool). Access: http://fisher.lib.virginia.edu/collections/state/ccdb

Warren, S., & Warren, K.G. (2001). Multiple Sclerosis. Geneva, Switzerland: World Health Organization.

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Lymetoo
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Wow! Pretty impressive! Thanks daystar!

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Opinions, not medical advice!

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firecop1066
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SHe is how old?
I just finished an advanced statistics class for my masters degree in criminal justice, and I HAVE NEVER seen anybody interpret stats like that, that was so GREAT....she ran the right stats, through the right program....I am REALLY impressed, that is Thesis/graduate level writing and staistics.

My hats are off to her that is impressive I hope my thesis on juvenile arrest and recidivism rates turns out that well....thank you for sharing this...Jill

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daystar1952
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I think at this point she is 16. She was 15 when she contacted me and began all this. Thanks so much for your input. I have no idea about statistics, etc. This past summer she went for classes at some impressive university....can't remember if it was Harvard or Brown.....something on that level. I think doctors and researchers need to pay attention to her and perhaps help guide her along.Hope she wins that contest...then maybe it will bring attention to this situation.
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lymeout
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Margie,
Do you know what prompted her to choose this topic? Please let her know that her "fan club" is cheering her on to win the competition! And will you update us, both on the competition and future plans of this young star?! Let's hope it includes more medical research!

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daystar1952
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I forget what really got her interested in this topic. Somehow she noticed a correlation and then went on from there. I wrote her and sent her all your wonderful comments. I'm sure she will be very pleased. And....I will keep you updated.....as long as she updates me. :-)
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MagicAcorn
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Pretty impressive work by a sophmore. A future attorney or doctor in the making. Go Megan!

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daystar1952
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Several doctors have already shown interest in Megan's paper. If anyone else knows of anyone...doctor...researcher, etc, who may want to read it, let me know.

Thanks so much
Margie

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Andie333
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Unlike firecop, I'm statistically challenged, but I was impressed by her writing.

And by her topic. She's clear, and the report is very compelling.

I work with high school kids and their writing. This really is an extraordinary effort, and I'd also love to know what prompted her to pick this topic.

You're right, daystar: the world needs this young woman!

Thanks so much for posting the report.

Andie

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trails
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I cant read it either! yikes...is there a way to see the cartography and graphs?? I am a visual person.

My partner teaches Statistics at the college level and I have sent her a copy. Hoping she can interpret for me and maybe use in her CLASS to teach others!!??? Will let you know if she can.

Way to GO! Megan Blewett!
[woohoo]

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bettyg
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Since my neuro brain won't let me read as is, I'm breaking it up as usual. Looks like others have save problem I do; so this will help them too.

Margie, perhaps you could show on your initial, long continuous post, that I have made shorter paragraphs for easier reading at the top of yours so they know there is something easier on this post for their eye/brain level comfort.

Or another possibility is to delete the contents & refer them to mine. I didn't change anything...just double spaced and made shorter paragraphs. Thanks for posting this Margie!

quote:
Originally posted by daystar1952:
Here we go...we'll see if it works. It's pretty huge with graphs and maps and all


A Geostatistical Analysis of Possible Spirochetal Involvement in Multiple Sclerosis and Other Related Diseases

Megan M. Blewett 2006
[email protected]


Abstract
A Geostatistical Analysis of Possible Spirochetal Involvement in Multiple Sclerosis and Other Related Diseases

Zoonotic diseases, especially those with insect or arthropod vectors, are recognized public health problems.

This class of diseases includes West Nile Virus, Human Granulocytic Ehrlichiosis (HGE), Babesiosis, Rocky Mountain Spotted Fever, and Lyme Disease.

This study examines whether Multiple Sclerosis (MS), which is the most common primary neurological disorder of young adults, also belongs in this category.

Visual and geostatistical analyses of MS and Lyme reveal striking similarities between the two diseases. Maps displaying each disorder's geographic distribution by county reveal this overlap visually.

In addition, the statistical correlation between MS and Lyme deaths (specifically all arthropod-borne disease deaths) is significant at the state-level and highly significant at the county-level.

MS incidence is known to vary with latitude; the study's statistical analysis reveals that Lyme Disease follows the same trend.

Discussion of possible biological explanations of these geographical and statistical trends is included in this article.

Significant correlations also exist with other diseases: on the state level, the correlation between MS and breast cancer is 0.330, and between MS and ALS (Motor Neuron Disease used in this study), the value is 0.618.

The control, external accident/injury, did not yield significant correlations.

Producing the maps and data required contacting all of the state epidemiologists in the nation for Lyme incidence data.

Compiling the data has resulted in one of the most comprehensive Lyme databases available to researchers.

The results of the visual, geostatistical, and biochemical analyses suggest common spirochetal involvement in MS and related diseases.

A Geostatistical Analysis of Possible Spirochetal Involvement in Multiple Sclerosis and Other Related Diseases

Introduction

Zoonotic diseases, especially those with insect or arthropod vectors, are well-recognized public health concerns.

Such diseases include West Nile Virus, Human Granulocytic Ehrlichiosis (HGE), Babesiosis, Rocky Mountain Spotted Fever, and Lyme Disease.

Multiple Sclerosis (MS) is the ``most common primary neurological disorder of young adults'' (Warren, 2001, page 1).

The National Multiple Sclerosis Society estimates that 400,000 people in the United States have MS (National Multiple Sclerosis Society, 2005).

The National Institute for Neurological Disorders and Stroke (NINDS) reports that the cause of MS is ``linked to an unknown environmental trigger, perhaps a virus (NINDS, 2006a).

Although a viral cause of MS is the prevailing view, some researchers believe MS is a zoonotic disease caused by a spirochete and spread by an arthropod vector. This study examines the spirochete hypothesis.

Spirochetal involvement in MS was a hypothesis gaining ground in Europe in the 1930s (Murray, 2005). Unfortunately, most of the research in support of this hypothesis, as well as the researchers themselves, was lost during World War II.

A surviving researcher, Gabriel Steiner, published work after World War II that identified a spirochete, Spirochaeta Myelophthora, as the causal agent of MS with an unknown vector (Steiner, 1952; Steiner, 1954).

Some of those who worked with Steiner in the United States as well as other researchers hypothesize that MS and Lyme might be either: 1) the same disease; or 2) different diseases caused by two different spirochetes carried by the same arthropod vector (Mattman, 2001; Rubel, 2003; Fritzsche, 2005).

Figure 1. Normalized Count of MS Deaths by County (1998 Deaths Divided by 1990 Census Population)

Figure 2. Normalized Count of Other Specified Arthropod-Borne Diseases (OSABD) Deaths by County (1998 Deaths Divided by 1990 Census Population)

Geostatistical and biochemical analyses reveal many similarities between MS and Lyme. Each is influenced by geography, and MS and Lyme overlap in this geographic distribution.

The author began to examine the relationship between MS and Lyme after being struck by the similarity of the distribution apparent in generated distribution maps of both diseases. See Figure 1 and Figure 2.

There are also biochemical similarities. NINDS (2006a) defines MS as ``An unpredictable disease of the central nervous system ... in which the body, through its immune system, launches a defensive attack against its own tissues ... the nerve-insulating myelin.''

NINDS (2006b) also recognizes the neurological complications of Lyme, which usually occur in the second stage, and include

``numbness, pain, weakness, Bell's palsy ... visual disturbances, and meningitis symptoms ... decreased concentration, irritability, memory and sleep disorders, and nerve damage in the arms and legs.''

Each of the disorders is characterized by damage to the blood-brain barrier (BBB) endothelium and subsequent increased barrier permeability (Pardridge, 1998).

Degradation of the barrier in Lyme patients involves bacterial breakdown of the collagen in the BBB basement membrane. The method of degradation in MS is not known (Russell, 1997), though thickness of the collagen layer could be a factor for prevalence among certain ethnic groups.

For example, African-Americans have high levels of collagen and low rates of MS. Both diseases also involve demyelination triggered by what can resemble an autoimmune attack against the myelin sheath.

Among MS patients, the mysterious increase in lymphocyte movement across the BBB could be in response to a bacterial invader.

Lastly, MS and Lyme disease share an inflammatory response, most likely the work of proinflammatory chemokines and cytokines(Rothwell, 2002).

The epidemiological and biochemical similarities suggest, but do not confirm a common bacterial basis for MS and Lyme.

The possibility of a common bacterial basis for both MS and Lyme is examined in this study using geostatistical analysis.

Such analysis combines descriptive and inferential statistical techniques with data visualization (cartographics).

The results have proven useful in understanding the etiology of many diseases including cholera, plague, malaria, smallpox, AIDS, and Lyme (Ormsby, 2001, Cliff, 2004; Koch, 2005;).

The hypothesis to be tested is that MS and Lyme Disease are triggered or influenced by a similar zoonotic spirochetal agent and spread by a tick-like vector.

If a common etiology exists, then a geostatistical relationship between Lyme and MS should be observed at either the state-level or the county-level or both.

The analysis can be improved by using a control variable (disease) and at least one other condition in which the causal agent or geographic distribution might be similar to that of MS.

The control variable in this study is accident/injury because this condition should be unrelated to a bacterial distribution.

The two diseases with a suggested bacterial cause or geographic similarity to MS are Breast Cancer (Cantwell, 1998) and Amyotrophic Lateral Sclerosis (ALS, Lou Gehrig's Disease) (Agency for Toxic Substances and Disease Registry, 2003).

Methods

Comparing disease distributions requires a database of the incidence of the diseases under examination and their associated environmental variables.

The data collection process began with a search for an authoritative source of incidence and prevalence data for Lyme, MS, Breast Cancer, ALS, and accidents/injuries.

Deaths recorded with the Centers for Disease Control and Prevention (CDC) and other government agencies provide an incidence measure of the given diseases.

A useful dataset was found on TheDataWeb, which is an online set of data libraries.

The dataset, ``Mortality - Underlying Cause-of-Death - 1998'' (United States Bureau of the Census (Census Bureau), 2005b; CDC, 2005c), was accessed via DataFerret, a data mining tool (Census Bureau, 2005a; CDC, 2005a).

The United States Bureau of the Census (Census Bureau) and the Centers Disease Control and Prevention (CDC) make both TheDataWeb and DataFerrett available to the public without charge.

This ``Mortality'' dataset contains geographic, demographic, and cause-of-death variables obtained from the death certificates of people who died in 1998.

Geographic variables include: county and state of residence, and county and state population. Cause-of-death-related variables include the underlying-cause-of-death coded using the International Classification of Diseases (ICD) Code (9th Revision).

The coding of death certificate information is standardized across all states. Death certificates are completed and filed at the state-level. (CDC, 2005b).

The death certificate information is collected from the states at the federal level by the National Center for Health Statistics (NCHS) and published along with other vital statistics as part of the National Vital Statistics System, ``the oldest and most successful example of inter-governmental data sharing in Public Health and the shared relationships, standards, and procedures form the mechanism by which NCHS collects and disseminates the Nation's official vital statistics.'' (CDC, 2005d, Introduction section).

``The vital statistics general mortality data are a fundamental source of demographic, geographic, and cause-of-death information.

This is one of the few sources of comparable health-related data for small geographic areas and a long time period in the United States.'' (Census Bureau, 2005c, National Center for Health Statistics section).

DataFerrett returns information from TheDataWeb in aggregate form only. Upon submitting a DataFerrett query for data the following use restriction statement is displayed:

WARNING! DATA USE RESTRICTIONS. Read Carefully Before Using

The Public Health Service Act (Section 308 (d) ) provides that the data collected by the National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC), may be used only for the purpose of health statistical reporting and analysis.

Any effort to determine the identity of any reported case is prohibited by this law. NCHS does all it can to ensure that the identity of data subjects cannot be disclosed.

All direct identifiers, as well as any characteristics that might lead to identifications, are omitted from the dataset.

Any intentional identification or disclosure of a person or establishment violates the assurances of confidentiality given to the providers of the information.

Therefore, users will:

● Use the data in this dataset for statistical reporting and analysis only.

● Make no use of the identity of any person or establishment discovered inadvertently and advise the Director, NCHS, of any such discovery.

● Not link this dataset with individually identifiable data from other NCHS or non-NCHS datasets.

By using the data you signify your agreement to comply with the above-stated statutorily based requirements.

Because DataFerrett queries use the ICD (9th Revision; ICD-9) codes as a selection criteria, the appropriate ICD-9 codes for each disease were determined through review of an online version of this document available from the National Center for Health Statistics (NCHS, 2005).

See Table 1 for a list of the ICD-9 codes used as selection criteria.

The Disease/Condition DataFerrett Selection Codes were then used to extract the state of residence for those who died in the United States in 1998 from each of the five diseases/conditions of interest.

Data was obtained for each of the fifty (50) states and the District of Columbia (total N for the state-level analyses = 51). This data was downloaded into an Excel file.

Added to this Excel file was the population of each state according to both the 1990 Census and the 2000 Census obtained from the Census Bureau American FactFinder, Population Finder website/webtool (Census Bureau, n.d.).

The total 1990 population from the Census Bureau and the total 1998 deaths from DataFerrett for each state were used to calculate the incidence variables used in the analyses.

See Table 2. The completed Excel file was opened and saved in SPSS (SPSS, 2003), which was used to calculate the descriptive and inferential statistics.

The SPSS file was saved as a Dbase IV file and then opened and saved in ArcGIS for the cartographic analyses.

The same general method was used to obtain data at the county level. However, in order to protect the privacy of individuals, DataFerrett does not return data for counties with less than 100,000 people according to the 1990 Census.

Instead, all death data for a state from counties with less than 100,000 is lumped into one value.

Wyoming, for example, has no counties with a population of more than 100,000 so the county-level death data for Wyoming is returned as one statewide number.

Disease/ Condition
Data Ferrett Selection Code
ICD-9 Categories and Code Descriptions

Multiple Sclerosis (MS) 340

Diseases of the Nervous System and Sense Organs (VI: 320-389),
Other Disorders of the Central Nervous System (340-349),
Multiple Sclerosis (340) - Includes Disseminated or Multiple Sclerosis:
Not Otherwise Specified (NOS), Brain Stem, Cord, Generalized

Lyme Disease 088.8

Infectious and Parasitic Diseases (I: 001-139),

Rickettsioses and Other Arthropod-Borne Diseases (080-088),

Other Arthropod-Borne Diseases (088),

Other Specified Arthropod-Borne Diseases (088.8),

Lyme Disease (088.81) - includes Erythema Chronicum Migrans, Babesiosis (088.82) - includes Babesiasis, Other (088.89).

NOTE: Lyme could not be selected individually because DataFerrett does not allow more detail in selection than 088.8, so analyses were done with this dataset for the category Other Specified Arthropod-Borne Diseases (OSABD) rather than Lyme alone.


Breast Cancer 174.0 - 174.9

Neoplasms (II: 140-239),

Malignant Neoplasm of the Female Breast (174) -Includes Nipple and Areola (174.0),

Central Portion (174.1), Upper-Inner Quadrant (174.2),

Lower-Inner Quadrant (174.3),

Upper-Outer Quadrant (174.4),

Lower-Outer Quadrant (174.5),

Axillary Tail (174.6),

Other (174.8),

and Breast, Unspecified (174.9)


Amyotrophic Lateral Sclerosis (ALS, Lou Gehrig's Disease) 335.2

Diseases of the Nervous System and Sense Organs (VI: 320-389),

Hereditary and Degenerative Diseases of the Central Nervous System (330-337),

Anterior Horn Cell Disease (335),

Motor Neuron Disease (335.2) - includes Amyotrophic Lateral Sclerosis, Progressive Muscular Atrophy (Pure), and Motor Neuron Disease (Bulbar) (Mixed Type).

NOTE: ALS could not be selected individually because ALS does not have its own ICD-9 code. The code for Motor Neuron Disease, which includes ALS was used for the analyses done with this dataset.

External Cause (CONTROL) E800 - E999

Supplementary Classification of External Causes of Injury and Poisoning (E800 -E999).

NOTE: Used as the Control Variable in the analyses.

Table 1. ICD-9 Code Used as the DataFerret Selection Criteria and Reasoning Variables

Calculation of Variable

MS Death Incidence per 100,000 Live (1990)

Number of deaths from MS in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.
MS Death Incidence per 100,000 Deaths (1998)

Number of deaths from MS in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.
OSABD Death Incidence per 100,000 Live (1990)

Number of deaths from OSABD in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.
OSABD Death Incidence per 100,000 Deaths (1998)

Number of deaths from OSABD in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.
1998 Lyme Incidence per 100,000 Live (1990)

Number of new Lyme cases reported by State Epidemiologists to the CDC for 1998 for that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.

1992-1998 Lyme Incidence per 100,000 Live (1990)

Total of the number of new Lyme cases reported by State Epidemiologists to the CDC for each of the years between 1992 and 1998 for that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.

Breast Cancer Death Incidence per 100,000 Live (1990)

Number of deaths from Breast Cancer in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit.

Breast Cancer Death Incidence per 100,000 Deaths (1998) Number of deaths from Breast Cancer in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.
Motor Neuron Death Incidence per 100,000 Live (1990)

Number of deaths from Motor Neuron Disease in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit
Motor Neuron Death Incidence per 100,000 Deaths (1998)

Number of deaths from Breast Cancer in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.


External Cause Death Incidence per 100,000 Live (1990) Number of deaths from External Causes in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the 1990 Census population for that geographic unit

External Cause Death Incidence per 100,000 Deaths (1998) Number of deaths from External Causes in 1998 as reported by DataFerrett in that geographic unit (state, county) divided by the total number of 1998 deaths from all causes reported by DataFerrett for that geographic unit.

Table 2. Calculation of Variables Used in the Dataset of Variables for Data Analysis
Delaware's three counties each have a population over 100,000 so county-level data is returned for all three Delaware counties.

New Jersey has twenty-one counties, but three of these counties have a population less than 100,000. For New Jersey, data is returned for each of eighteen individual counties and then one number is returned for the three counties (combined) with a population of less than 100,000.


There are 3141 counties in the United States, but DataFerrett returns data on 504, which includes the combined values for a state's less-than-100,000 counties.

At the county-level, the population data was obtained from Census data available through the University of Virginia (n.d.).

County-level analyses were also done using only those states generally considered to have a high Lyme incidence (Lyme-State).

These 123 Lyme-State counties, which include those counties lumped together because of a less-than-100,000 population, are in the following ten states:

Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, and Vermont.

All statistical calculations were done using SPSS. Counts of disease deaths provided by the CDC were normalized by the 1990 Census population information, yielding number of deaths due to a certain disease per 100,000 people in that state or county.

See Table 2. But normalizing disease deaths by the number of living people in a state or county produced the confounding factor of that geographic unit's demographics and age.

So a new measurement was introduced: the number of deaths from each disease was divided over the total deaths of each county or state (incidence of death due to a specific disease per 100,000 deaths in that geographic unit). See Table 2.

Another confounding factor was the exclusion of counties with fewer than 100,000 residents due to CDC privacy policy.

To accommodate for this, the total deaths from all of these smaller counties was smeared proportionally across each county included in the set.

This set of all the counties with fewer than 100,000 people was labeled a ``super-county''. The analysis could use these blocks in combination or independently.

To this data, in both the state and county files, was added the number of new Lyme cases reported each year from 1992-1998, centroid latitude, centroid longitude, and population elevation (the elevation of the county seat or the nearest population center to the county seat for which there is elevation data).

Centroid latitude and longitude were averaged over all counties in a state to calculate the state value.

The same method was used to calculate each state's population elevation.

Centroid latitude, centroid longitude, and most population elevation information were obtained from the United States Geological Survey (USGS, n.d.).

The Lyme case data was added because the death data from DataFerrett includes more than Lyme (See Table 1). The DataFerrett category that includes Lyme deaths is ``Other Specified Arthropod Borne Diseases'' in ICD-9. This category variable is named OSABD in this study.

The number of Lyme cases in each state for the years 1992-1998 is available from CDC publications (CDC, 2002).

The number of Lyme cases per year by county is not, however, available from the CDC. Although the CDC publishes some multi-year cartographic material by county, the CDC does not report county-level, annual numerical data for a state to the public.

County-level Lyme incidence data is only available to the public by contacting each state's department of health, specifically, the state epidemiologist.

In this study, Lyme data available by county was subsequently compiled to match the super-counties data available for DataFerrett death data.

The process of obtaining Lyme incidence data by county for the years 1992 through and including 1998 was labor-intensive.

Each state's Department of Health website was visited to see if the needed Lyme data was available on the website.

If the data was not available, that state's epidemiologist was emailed using contact information from the Council of State and Territorial Epidemiologists (n.d.) website provided by the CDC.

Most epidemiologists contacted via email responded and provided the necessary data.

All of these sources were recorded and the data compiled and added to the database.

As of this writing, this appears to be the most comprehensive database of Lyme in existence.

Results

Descriptive statistics for the variables in each of the three basic datasets can be found in Table 3, Table 4, and Table 5.

As many statistical tests assume that the data are normally distributed, each variable's skewness and kurtosis values and standard errors were examined.

A normally distributed variable has a value of 0 for both skewness (a measure of symmetry) and kurtosis (a measure of clustering around a central point).

If the ratio of the skewness value to its standard error is between -2 and +2, then the distribution is symmetrical (normal).

If the ratio of the kurtosis value to its standard error is between -2 and +2, then the data are normally distributed. (SPSS, 2003; Norusis, 2003).

Few of the variables are normally distributed.

In the State-Level variables, only MS Death Incidence per 100,000 Live (1990), MS Death Incidence per 100,000 Deaths (1998), Motor Neuron Death Incidence per 100,000 Live (1990), Motor Neuron Death Incidence per 100,000 Deaths (1998), and External Cause Death Incidence per 100,000 Live (1990) are normally distributed.


In the Lyme-State County Level (Population >= 100,000) variables, only MS Death Incidence per 100,000 Live (1990) and Breast Cancer Death Incidence per 100,000 Deaths (1998) are normally distributed.

The next step in the analysis was a correlation analysis. Calculating a Pearson correlation coefficient (r) is appropriate for variables that are normally distributed. (SPSS, 2003, page 379).

Calculating a Kendall's tau-b or Spearman's rho is appropriate when the data are not normally distributed.

Because all three of these correlation analyses assume a linear relationship between the variables, a scatterplot graph was constructed for each pair of variables to be analyzed.

Each scatterplot was linear so a Pearson's, Kendall's, or Spearman's coefficient was calculated as appropriate for pairs of variables in each of the three datasets. The results can be seen in Table 6, Table 7, and Table 8.

Multiple regression was also used to find the model that would best predict the MS Death Incidence per 100,000 Deaths.

All variables contained in the dataset were entered into the regression analysis using the stepwise feature.

All variable values were converted to z-scores for use in the regression analysis. These results can be seen in Table 9.

Lastly, cartographic analyses were completed. These can be seen in Figure 1, Figure 2, and Figure 3. They show the normalized distribution of MS Deaths, OSABD Deaths, and External Causes Deaths, respectively.


Dataset of State-Level Disease and Geographic Variables N Min Max Mean Std. Dev. Skewness Kurtosis
Value Std. Err. Value Std. Err.

MS Death Incidence per 100,000 Live (1990) 51 0.1 2.0 1.1 0.4 0.2 0.3 0.5 0.7

MS Death Incidence per 100,000 Deaths (1998) 51 12.4 219.6 112.8 43.7 0.3 0.3 -0.1 0.7

OSABD Death Incidence per 100,000 Live (1990) 51 1.5 7.2 3.6 1.6 0.8 0.3 -0.5 0.7

OSABD Death Incidence per 100,000 Deaths (1998) 51 159.0 803.9 385.0 166.6 0.9 0.3 0.1 0.7

1998 Lyme Incidence per 100,000 Live (1990) 51 0.0 104.5 6.7 18.6 4.2 0.3 18.9 0.7

1992-1998 Lyme Incidence per 100,000 Live (1990) 51 0.0 472.2 33.6 83.7 3.9 0.3 16.8 0.7

Breast Cancer Death Incidence per 100,000 Live (1990) 51 8.9 22.1 16.8 2.3 -0.6 0.3 1.9 0.7

Breast Cancer Death Incidence per 100,000 Deaths (1998) 51 1377.7 2213.4 1772.1 186.6 0.3 0.3 2.1 0.7

Motor Neuron Death Incidence per 100,000 Live (1990) 51 0.7 2.7 1.8 0.5 0.0 0.3 -0.4 0.7

Motor Neuron Death Incidence per 100,000 Deaths (1998) 51 98.9 303.1 187.1 45.1 0.2 0.3 -0.0 0.7

External Cause Death Incidence per 100,000 Live (1990) 51 39.8 109.8 66.1 15.8 0.4 0.3 0.1 0.7

External Cause Death Incidence per 100,000 Deaths (1998) 51 4227.4 16802.8 7067.0 2068.1 2.3 0.3 9.0 0.7

Population Elevation (feet) 51 18.0 6305.4 1337.7 1602.0 1.8 0.3 2.3 0.7

Centroid Latitude 51 21.0 60.3 39.5 5.9 0.1 0.3 3.2 0.7

Centroid Longitude 51 -157.3 -69.5 -93.4 19.0 -1.3 0.3 2.1 0.7


Table 3. Descriptive Statistics for the Dataset of State-Level Disease and Geographic Variables

Dataset of County-Level (Population >=100,000) Disease and Geographic Variables N Min Max Mean Std. Dev. Skewness Kurtosis
Value Std. Err. Value Std. Err.

MS Death Incidence per 100,000 Live (1990) 504 0.0 4.3 1.0 0.7 0.9 0.1 1.5 0.2

MS Death Incidence per 100,000 Deaths (1998) 504 0.0 523.6 112.2 81.4 0.9 0.1 1.9 0.2

OSABD Death Incidence per 100,000 Live (1990) 504 0.0 14.9 3.3 2.1 1.5 0.1 4.0 0.2

OSABD Death Incidence per 100,000 Deaths (1998) 504 0.0 1905.0 354.2 221.5 1.6 0.1 5.2 0.2

1998 Lyme Incidence per 100,000 Live (1990) 504 0.0 485.8 10.3 43.2 7.3 0.1 61.0 0.2

1992-1998 Lyme Incidence per 100,000 Live (1990) 504 0.0 2743.6 51.1 213.4 7.6 0.1 71.0 0.2

Breast Cancer Death Incidence per 100,000 Live (1990) 504 1.8 35.1 16.7 4.1 0.5 0.1 1.5 0.2

Breast Cancer Death Incidence per 100,000 Deaths (1998) 504 221.0 3081.5 1815.4 368.6 0.0 0.1 0.8 0.2

Motor Neuron Death Incidence per 100,000 Live (1990) 504 0.0 6.4 1.8 1.1 0.7 0.1 0.9 0.2

Motor Neuron Death Incidence per 100,000 Deaths (1998) 504 0.0 661.0 193.5 112.8 0.8 0.1 1.2 0.2

External Cause Death Incidence per 100,000 Live (1990) 504 25.0 140.2 59.4 17.9 1.0 0.1 1.8 0.2

External Cause Death Incidence per 100,000 Deaths (1998) 504 2970.3 18308.1 6477.6 1831.9 1.3 0.1 4.2 0.2

Population Elevation (feet) 504 -40.0 6485.9 753.6 1090.8 3.0 0.1 9.6 0.2

Centroid Latitude 504 19.5 61.2 38.3 5.2 -0.38 0.1 1.2 0.2

Centroid Longitude 504 -158.0 -68.7 -89.6 16.0 -1.3 0.1 1.7 0.2


Table 4. Descriptive Statistics for the Dataset of County-Level (Population >= 100,000) Disease and Geographic Variables

Dataset of Lyme State County-Level (Population >=100,000) Disease and Geographic Variables N Min Max Mean Std. Dev. Skewness Kurtosis

Value Std. Err. Value Std. Err.

MS Death Incidence per 100,000 Live (1990) 123 0.0 2.8 1.0 0.6 0.4 0.2 0.2 0.4

MS Death Incidence per 100,000 Deaths (1998) 123 0.0 412.1 107.2 71.5 0.8 0.2 2.0 0.4

OSABD Death Incidence per 100,000 Live (1990) 123 0.0 7.2 2.6 1.3 1.0 0.2 1.6 0.4

OSABD Death Incidence per 100,000 Deaths (1998) 123 0.0 815.3 275.0 139.8 0.9 0.2 1.4 0.4

1998 Lyme Incidence per 100,000 Live (1990) 123 0.0 485.8 35.4 73.3 3.7 0.2 16.0 0.4

1992-1998 Lyme Incidence per 100,000 Live (1990) 123 2.4 2743.6 176.6 378.7 4.0 0.2 19.9 0.4

Breast Cancer Death Incidence per 100,000 Live (1990) 123 9.9 35.1 18.3 3.7 0.9 0.2 3.1 0.4

Breast Cancer Death Incidence per 100,000 Deaths (1998) 123 1061.0 3081.5 1957.8 339.1 0.2 0.2 0.5 0.4

Motor Neuron Death Incidence per 100,000 Live (1990) 123 0.0 4.8 1.8 1.1 0.7 0.2 0.3 0.4

Motor Neuron Death Incidence per 100,000 Deaths (1998) 123 0.0 632.2 198.4 120.9 1.1 0.2 1.8 0.4

External Cause Death Incidence per 100,000 Live (1990) 123 25.0 118.5 47.7 12.6 1.6 0.2 7.2 0.4

External Cause Death Incidence per 100,000 Deaths (1998) 123 2970.3 10056.5 5077.8 1126.6 1.6 0.2 5.5 0.4

Population Elevation (feet) 123 9.0 2140.0 341.7 355.9 1.9 0.2 5.0 0.4

Centroid Latitude 123 38.5 45.2 41.3 1.5 0.5 0.2 -0.3 0.4

Centroid Longitude 123 -80.5 -68.7 -74.9 2.6 -0.1 0.2 -0.3 0.4


Table 5. Descriptive Statistics for the Dataset of Lyme State County-Level (Population >= 100,000) Disease and Geographic Variables


Statistically Significant Correlations in the Dataset of State-Level Disease and Geographic Variables (MS Variables and Other Variables) N

MS Death Incidence per 100,000 Live (1990)

MS Death Incidence per 100,000 Deaths (1998)

OSABD Death Incidence per 100,000 Live (1990) 51 Kendall's tau_b: 0.213*

Sig. (2-tailed): 0.028
Spearman's rho: 0.293*
Sig. (2-tailed): 0.037 No statistically significant correlation.


Breast Cancer Death Incidence per 100,000 Deaths (1998) 51 No statistically significant correlation Kendall's tau_b: 0.222*
Sig. (2-tailed): 0.022
Spearman's rho: 0.330*
Sig. (2-tailed): 0.018


Motor Neuron Death Incidence per 100,000 Live (1990) 51 Pearson: 0.569**
Sig. (2-tailed): 0.000 Pearson: 0.413**
Sig. (2-tailed): 0.003


Motor Neuron Death Incidence per 100,000 Deaths (1998) 51 Pearson: 0.628**
Sig. (2-tailed): 0.000 Pearson: 0.618**
Sig. (2-tailed): 0.000

Population Elevation (feet) 51 Kendall's tau_b: 0.269**
Sig. (2-tailed): 0.005
Spearman's rho: 0.404**
Sig. (2-tailed): 0.003 Kendall's tau_b: 0.286**
Sig. (2-tailed): 0.003
Spearman's rho: 0.401**
Sig. (2-tailed): 0.004

Centroid Latitude 51 Kendall's tau_b: 0.522**
Sig. (2-tailed): 0.000
Spearman's rho: 0.669**
Sig. (2-tailed): 0.000 Kendall's tau_b: 0.529**
Sig. (2-tailed): 0.000
Spearman's rho: 0.692**
Sig. (2-tailed): 0.000

Table 6. Statistically Significant Correlations in the Dataset of State-Level Disease and Geographic Variables (MS Variables and Other Variables)


Discussion

The results of the statistical analyses support geographically the proposed connection between Multiple Sclerosis, Lyme, and related diseases.

The cartographic display in Figure 1 and Figure 2 show a clear similarity between MS and OSABD, which includes Lyme.

Figure 3, which displays the control variable, is very different. The correlations and regression analysis also show a clear geographic co-occurrence of MS and Lyme.

Because there is no such relationship with the control variable, External Deaths, a common cause for MS and Lyme is suggested.

The strong association of MS with Motor Neuron Disease (ALS) and the weaker, but significant, association with

Statistically Significant Correlations in the Dataset of County-Level (Population >=100,000)

Disease and Geographic Variables (MS Variables and Other Variables) N MS Death Incidence per 100,000 Live (1990) MS Death Incidence per 100,000 Deaths (1998)

OSABD Death Incidence per 100,000 Live (1990) 504 Kendall's tau_b: 0.119**
Sig. (2-tailed): 0.000
Spearman's rho: 0.174**
Sig. (2-tailed): 0.000 Kendall's tau_b: 0.068*
Sig. (2-tailed): 0.023
Spearman's rho: 0.101**
Sig. (2-tailed): 0.024


OSABD Death Incidence per 100,000 Deaths (1998) 504 Kendall's tau_b: 0.064*
Sig. (2-tailed): 0.035
Spearman's rho: 0.094*
Sig. (2-tailed): .0360 Kendall's tau_b: 0.079**
Sig. (2-tailed): 0.009
Spearman's rho: 0.114**
Sig. (2-tailed): 0.010


Breast Cancer Death Incidence per 100,000 Live (1990) 504 Kendall's tau_b: 0.144**
Sig. (2-tailed): 0.000
Spearman's rho: 0.209**
Sig. (2-tailed): 0.000 No statistically significant correlation.


Breast Cancer Death Incidence per 100,000 Deaths (1998) 504 No statistically significant correlation. Kendall's tau_b: 0.099**
Sig. (2-tailed): 0.001
Spearman's rho: 0.146**
Sig. (2-tailed): 0.001


Motor Neuron Death Incidence per 100,000 Live (1990) 504 Kendall's tau_b: 0.134**
Sig. (2-tailed): 0.000
Spearman's rho: 0.183**
Sig. (2-tailed): 0.000 Kendall's tau_b: 0.076*
Sig. (2-tailed): 0.011
Spearman's rho: 0.106*
Sig. (2-tailed): 0.017


Motor Neuron Death Incidence per 100,000 Deaths (1998) 504 Kendall's tau_b: 0.091**
Sig. (2-tailed): 0.002
Spearman's rho: 0.125**
Sig. (2-tailed): 0.005 Kendall's tau_b: 0.114**
Sig. (2-tailed): 0.000
Spearman's rho: 0.155**
Sig. (2-tailed): 0.000


External Cause Death Incidence per 100,000 Live (1990) 504 No statistically significant correlation. Kendall's tau_b: -0.073*
Sig. (2-tailed): 0.016
Spearman's rho: -0.108*
Sig. (2-tailed): 0.015


External Cause Death Incidence per 100,000 Deaths (1998) 504 Kendall's tau_b: -0.079**
Sig. (2-tailed): 0.009
Spearman's rho: -0.117**
Sig. (2-tailed): 0.008 No statistically significant correlation.


Centroid Latitude 504 Kendall's tau_b: 0.173**
Sig. (2-tailed): 0.000
Spearman's rho: 0.249**
Sig. (2-tailed): 0.000 Kendall's tau_b: 0.203**
Sig. (2-tailed): 0.000
Spearman's rho: 0.296**
Sig. (2-tailed): 0.000


Table 7. Statistically Significant Correlations in the Dataset of County-Level (Population >=100,000) Disease and Geographic Variables (MS Variables and Other Variables)


Statistically Significant Correlations in the Basic Set of Dataset of Lyme State County-Level (Population >=100,000) Disease and Geographic Variables (MS Variables and Other Variables) N

MS Death Incidence per 100,000 Live (1990) MS Death Incidence per 100,000 Deaths (1998)


Breast Cancer Death Incidence per 100,000 Deaths (1998) 123 No statistically significant correlation. Kendall's tau_b: 0.152*
Sig. (2-tailed): 0.014
Spearman's rho: 0.221*
Sig. (2-tailed): 0.014


External Cause Death Incidence per 100,000 Live (1990) 123 No statistically significant correlation. Kendall's tau_b: -0.152*
Sig. (2-tailed): 0.013
Spearman's rho: -0.221*
Sig. (2-tailed): 0.014


Centroid Latitude 123 Kendall's tau_b: 0.136*
Sig. (2-tailed): 0.027
Spearman's rho: 0.199*
Sig. (2-tailed): 0.027 Kendall's tau_b: 0.134*
Sig. (2-tailed): 0.029
Spearman's rho: 0.196*
Sig. (2-tailed): 0.029


Centroid Longitude 123 Kendall's tau_b: 0.129*
Sig. (2-tailed): 0.035
Spearman's rho: 0.192*
Sig. (2-tailed): 0.033 Kendall's tau_b: 0.149*
Sig. (2-tailed): 0.016
Spearman's rho: 0.226*
Sig. (2-tailed): 0.012


Table 8. Statistically Significant Correlations in the Dataset of Basic Set of Lyme State County-Level (Population >=100,000) Disease and Geographic Variables (MS Variables and Other Variables)


Dependent Variable Independent Variables R Square

State-Level (N=51) Z-Score of MS Death Incidence per 100,000 Deaths (1998) Constant = -4.539E-16
Z-Score of Motor Neuron Death Incidence per 100,000 Deaths (1998) (B = .354)

Z-Score of Centroid Latitude (B = .378)

Z-Score of OSABD Death Incidence per 100,000 Deaths (1998) (B = .259) .554
County-Level (N=504) Z-Score of MS Death Incidence per 100,000 Deaths (1998) Constant = .196

Z-Score of Centroid Latitude (B = .406)

Z-Score of OSABD Death Incidence per 100,000 Deaths (1998) (B = .200)

Z-Score of Breast Cancer Death Incidence per 100,000 Deaths (1998) (B = .099) .109

Lyme State County-Level (N=123) Z-Score of MS Death Incidence per 100,000 Deaths (1998) Constant = -.867

Z-Score 1992-1998 Lyme Incidence per 100,000 Live (1990) (B = .176)

Z-Score of Breast Cancer Death Incidence per 100,000 Deaths (1998) (B = .210)
Z-Score of Centroid Latitude (B = 1.051) .134

Table 9. Multiple Regression Analysis of Z-Score of MS Death Incidence per 100,000 Deaths (1998) Variable at the State-Level, County-Level, and Lyme State County-Level: All Basic Set Variables Included in the Stepwise Analysis

Figure 3. Normalized Count of External Causes of Death by County (1998 Deaths Divided by 1990 Census Population)


Breast Cancer, also suggest a possible common environmental, spirochetal, mechanism for these diseases.

The well-known relationship between latitude and MS (Warren, 1998) is also seen in these analyses.

This relationship is also statistically significant for both Breast Cancer and Motor Neuron Disease at the state and county level as well as Lyme at the county level.

The overlap between MS and Lyme is not solely geographic; the results of the statistical analyses can be explained using biochemical principles as well.

Both diseases involve vascular inflammation within the Central Nervous System (CNS) caused in part by inflammatory cytokines and chemokines (Pardridge, 1998).

Tissue plasminogen activator (tPA) regenerates plasmin and allows penetration not only by
bacteria but by other invaders as well (Pardridge, 1998).

Borrelia Burgdorferi, the causative bacterial agent of Lyme Disease, uses tPA in order to degrade the collagen layer of the Blood-brain barrier (BBB) and enter the CNS.

Likewise, tPA is found in the MS BBB, though its role is currently unknown (Pardridge, 1998).

Once the unknown invader moves within the CNS, one of the first responses to attack is the clustering of macrophages around the sclerotic plaques of MS.

Macrophages have two main functions: to digest dead cell material and to digest bacteria by phagocytosis (Guyton, 1997).

While the macrophages might be serving to break down remnants of myelin already attacked by the unknown antigen, the macrophages' secretion of Nitrogen Monoxide (NO) seems to suggest that some bacteria is also present.

NO plays a number of different roles in disease, both positive and negative; it may induce axonal degeneration or vascular dilation, serve as a signaling molecule between neurons, affect memory and thought processes of the brain, or kill bacteria (Guyton, 1997).

Parallels exist not only in Lyme Disease but within other diseases as well.

One example is Leishmania, a parasitic disease which affects the body's internal organs and immune system. The macrophages involved secrete NO to kill the antigen, a protozoan (CDC, 2004). A similar mechanism against a spirochetal invader could be at work in MS.

Lyme resembles MS more and more as it progresses within the body. In its most developed stages, it mimics an autoimmune attack against the myelin sheath, which is what most researchers believe MS to be (Filley, 2001).

But the autoimmune theory does not explain very well the relapse-remitting progression common in both MS and Lyme.

If in fact the T cells and the body's immune cells are primed not to attack an unknown invader but to attack the Myelin Basic Protein (MBP) or some other feature of the fatty sheath surrounding the axons, then one would not expect the disease to remit when there is still myelin left to be digested.

The presence of spirochetes seems to provide a reasonable solution. Lyme follows a relapse-remitting progression due to the many different forms that spirochetes such as Borrelia Burgdorferi are known to take.

When the environment is positive for the spirochetal activity, the bacteria remain in a fully elongated form (about 5-20 μm in length), but in the presence of antibiotics many spirochetes defensively curl up into a granular form (about .3-.5μm) (Mattman, 2001).

While in the granular form, the spirochetes are virtually undetectable even by electron microscopy, and the disease appears to be latent for some time. This latency period, though, is perhaps the most deleterious stage of disease.

While in their highly minimized forms, the spirochetes are able to traverse many of the body's pores and enter into cells and organs (Saier, 2001). When no longer threatened, they expand again into their elongated form.

Spirochetes thrive upon steroids, yet most MS medications use steroids to reduce neural inflammation (Russell, 1997).

The steroids could be playing additional roles if MS is in fact influenced by spirochetes.

Although spirochetes thrive in the presence of steroids, the steroids could bring about the bacteria's destruction. Acting as a sort of bait, often steroids cause spirochetes expand into their elongated forms, though in this form the bacteria are much more susceptible to T-cell attack (Mattman, 2001).

This could explain the success of steroids as a medication and provide some insight for developing more permanent solutions.

Spirochetes may also act as a gateway for certain types of cancer. Because the spirochetes are so amorphous, they can mimic the body's own cells.

Looking life self-material, the bacteria manage to fuse with the cell walls and from there eventually control the activities of the cell, often resulting in cancer (Mattman, 2001).

The statistical correlations agree with this; the correlation between MS and breast cancer is significant.

One testament to the connection between MS and Lyme is the difficulty that doctors face in distinguishing between the two when making a diagnosis. In certain cases, patients are misdiagnosed several times.

Both diseases can produce MRI's marked by sclerotic plaques, and both manifest similar symptoms such as memory lapses, fatigue, and joint pain (Warren, 2001).

The age of onset of MS is typically between 35 and 40 years of age. Likewise, one of the peak age groups to acquire Lyme Disease falls in this range.

Epidemiological studies tracking the movement of MS patients from areas of higher MS incidence to lower MS incidence have revealed that the unknown trigger for MS is most likely encountered around twelve years of age (Warren, 2001).

Similarly, the majority of Lyme patients acquire the disease when they are in this stage of adolescence.

This suggests that MS might develop from a secondary spirochete bite, though other factors such as stress and natural aging could also trigger its onset.

The study, though, is not free of confounding factors. In studies of this nature, one must worry about spurious correlations. The control (external accident/injury) seems reasonable.

Secondly, the geographical distribution of MS and Lyme deaths represents not only the presence of an etiological agent, but social trends as well.

Often people diagnosed with chronic illnesses move to other, more hospitable regions of the United States like Florida or California, or to regions with better healthcare such as states along the East Coast, particularly for MS.

The use of death rates rather than diagnosis rates provided more definitive information, though it introduced the variable of healthcare.

States which have higher rates of diagnosis, in fact, sometimes display lower death rates, because, with experience, doctors in those areas often are more familiar with treating the disease.

Excluding counties with less than 100,000 residents also presented confounding factors.

Because Lyme is known to be transmitted by ticks in wooded areas, much of the Lyme incidence occurs in more rural counties in which the boundary between people and nature is less well defined.

As mentioned previously, for reasons of confidentiality, for each state the CDC released only total deaths of all the counties with less than 100,000 people.

This introduced a smearing effect in which some vital Lyme information may have been washed out.

Nonetheless, sufficient similarities exist in this study to suggest, but not confirm, a common spirochetal basis for MS and Lyme.

References

Agency for Toxic Substances and Disease Registry (ATSDR). (2003, last updated May). Multiple Sclerosis and Amyotrophic Lateral Sclerosis-Related Projects: Ongoing and Completed Projects, Health Investigations Branch, Division of Health Studies. Access at:

http://www.atsdr.cdc.gov/DHS/MS_Fact_Sheet.html

Cantwell, A. (1998). Do killer microbes cause Breast Cancer? New Dawn: A Journal of Alternative News and Information, 48 (electronic copy). Available at:

http://www.newdawnmagazine.com/Articles/Do%20Killer%20Microbes%20Cause%20Breast%20Cancer.html

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). (2005a). DataWarehouse, Accessing NCHS Data in DataFerrett. Access:

http://www.cdc.gov/nchs/datawh/ferret/ferret.htm

Centers for Disease Control and Prevention (CDC). (2002). Lyme Disease - United States, 2000. Morbidity and Mortality Weekly Report (MMWR), 51(02), 29-31.

Centers for Disease Control and Prevention (CDC), Division of Parasitic Diseases (2004, last updated April). Parasitic Disease Information: Leishmania Infection Factsheet. Access:
http://www.cdc.gov/ncidod/dpd/parasites/leishmania/factsht_leishmania.htm

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). (2005b). Mortality Data from the National Vital Statistics System. Access:

http://www.cdc.gov/nchs/deaths.htm

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), Publications and Information Products. (2005c). Mortality Data and Underlying Cause of Death Public-Use Files. Access:

http://www.cdc.gov/nchs/products/elec_prods/subject/mortucd.htm

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). (2005d). National Vital Statistics System. Access: http://www.cdc.gov/nchs/nvss.htm

Cliff, A., Haggett, P, & Smallman-Raynor, M. (2004). World Atlas of Epidemic Diseases. New York: Oxford University Press.

Council of State Epidemiologists. (n.d.). Directory. Access:

http://www.cste.org/members/state_and_territorial_epi.asp and http://www.cste.org

Filley, C.M. (2001). The Behavioral Neurology of White Matter. New York: Oxford University Press.

Fritzsche, M. (2005). Chronic lyme borreliosis at the root of multiple sclerosis - is a cure with antibiotics attainable? Medical Hypotheses, 64(3), 438-448.

Guyton, A.C. & Hall, J.E. (1997) Human Physiology and Mechanisms of Disease (6th Edition). Philadelphia: W.B. Saunders

Koch, T. (2005). Cartographics of Disease: Maps, Mapping, and Medicine. California: ESRI Press.

Mattman, L.H. (2001). Cell Wall Deficient Forms: Stealth Pathogens (Third Edition). New York: CRC Press.

McKinnell, R.G., Parchment, R.E., Perantoni, A.O., & Pierce, G.B. (2003). The Biological Basis of Cancer. Cambridge: Cambridge University Press.

Murray, T.J. (2005). Multiple Sclerosis: The History of a Disease. New York: Demos Medical Publishing.

National Center for Health Statistics. (2005, last updated August). Mortality Data from the National Vital Statistics System, International Classification of Diseases, Ninth Revision (ICD-9), Volume I. Accessed at

ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD-9/

National Multiple Sclerosis Society. (2005, last updated October). Epidemiology. Retrieved

from http://www.nationalmssociety.org/Sourcebook-Epidemiology.asp

National Institute of Neurological Disorders and Stroke (NINDS). (2006a, last updated January). NINDS Multiple Sclerosis Information Page. Retrieved from

http://www.ninds.nih.gov/disorders/multiple_sclerosis/multiple_sclerosis.htm

National Institute of Neurological Disorders and Stroke (NINDS). (2006b, last updated January). NINDS Neurological Complications of Lyme Disease Information Page. Retrieved from

http://www.ninds.nih.gov/disorders/lyme/lyme.htm

Norusis, M.J. (2003). SPSS 12.0 Statistical Procedures Companion. Upper Saddle River, New Jersey: Prentice Hall.

Ormsby, T., Napoleon, E., Burke, R., Groessl, C., & Feaster, L. (2001). Getting to Know ArcGIS Desktop: Basics of ArcView, ArcEditor, and ArcInfo. California: ESRI Press.

Pardridge, W.M. (Ed.). (1998). Introduction to the Blood-Brain Barrier: Methodology, Biology, and Pathology. Cambridge: Cambridge University Press.

Rothwell, N. & Loddick, S. (Ed.). (2002). Immune and Inflammatory Responses in the Nervous System (Second Edition). Oxford: Oxford University Press.

Rubel, J. (Ed.). (2003). Lyme disease survival in adverse conditions: the strategy of morphological variation in Borrelia burgdorferi & other spirochetes 1900-2001 (electronic). Lyme Info: Cystic Form of Bb & Other Spirochetes: Advanced. Accessed at

http://www.lymeinfo.net/medical/LDAdverseConditions.pdf

Russell, W.C. (Ed.). (1997). Molecular Biology of Multiple Sclerosis. New York: John Wiley &Sons.

Saier, M.H. & Garcia-Lara, J. (2001). The Spirochetes: Molecular and Cellular Biology. Wiltshire: United Kingon: Horizon Press.

SPSS. (2003). SPSS Base 12.0 User's Guide. Chicago, Illinois: Author.
Steiner, G. (1952). Acute plaques in multiple sclerosis, their pathogenic significance and the role of spirochetes as etiological factor. Journal of Neuropathology and Experimental Neurology, 11(4), 343-372

Steiner, G. (1954). Morphology of spirochaeta myelophthora in multiple sclerosis. Journal of Neuropathology, 13, 221-229.

United States Census Bureau. (n.d.). American FactFinder, Population Finder (Data WebTool). Access:

http://factfinder.census.gov/servlet/SAFFPopulation?_submenuId=population_0&_sse=on

United States Census Bureau and Centers for Disease Control and Prevention (CDC). (2005a). DataFerrett: For TheDataWeb. Access:

http://dataferrett.census.gov/index.html.

United States Census Bureau and Centers for Disease Control and Prevention (CDC). (2005b). National Center for Health Statistics, Mortality - Underlying Cause of Death, 1998 [Data WebTool]. Available from TheDataWeb website,

http://www.thedataweb.org/index.html.

United States Census Bureau and Centers for Disease Control and Prevention (CDC). (2005c). TheDataWeb: Description of Datasets Available Using DataFerrett. Access:

http://www.thedataweb.org/datasets.html

United States Geological Survey (n.d.). Geographic Names Information System (Data WebTool). Access:

http://geonames.usgs.gov/fips55/fips55down.html

University of Virginia. (n.d.). University of Virginia Library Geostat Center: Collections for the 1990 and 2000 Populations (Data WebTool). Access:

http://fisher.lib.virginia.edu/collections/state/ccdb

Warren, S., & Warren, K.G. (2001). Multiple Sclerosis. Geneva, Switzerland: World Health Organization.

Wow, I can't believe what she came up with at age 15! I edited just making shorter paragraphs so all us neuro lymies could read/comprehend; it took me between 60-90 minutes as I was reading it after I made shorter paragrahs.

Margie, could you email her back with what I did editing for shorter paragraphs; it may help others she has contact with.

Please tell her how impressive it was; I'm going to share with an online friend with severe, agressive MS and lyme. Perhaps she could learn something to try to have her drs. use on her!

I copied this to my wordperfect; came out to be 29 pages the way I spaced above for anyone printing this out!

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Ann-OH
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This is a remarkable paper from a remarkable young woman!

Betty, You have to follow a certan form of writing when you do a scientific paper. The writer has to be careful to follow that. Judges of the paper will be looking for how it appears on the page and how it follows scientific writing style. So the writer can't break it up to make it more readable.

That said: I am very glad you split it up for Lyme-effected readers. Thank you for all the good time you spent on this job!

Ann - OH

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lingolady
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Thanks, daystar!!(And thanks, Betty, for the easier-to-read transcription) What an impressive piece of work! A lot of high schools have programs for their students to engage in high level research now. As a person diagnosed with MS, then lyme, I fervently hope that Megan wins this contest and that it causes the medical establishment to think outside of their small boxes.

Isn't it great what open minds and pure,unbiased research can discover?

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lingolady
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Thanks, daystar!!(And thanks, Betty, for the easier-to-read transcription) What an impressive piece of work! A lot of high schools have programs for their students to engage in high level research now. As a person diagnosed with MS, then lyme, I fervently hope that Megan wins this contest and that it causes the medical establishment to think outside of their small boxes.

Isn't it great what open minds and pure,unbiased research can discover?

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5dana8
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Thanks Daystar!!

[woohoo]

Way to go!!

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bettyg
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Update: I justed edited my 1st response when I asked Margie to copy/paste Megan's paper here.

It told of Megan's SINGLE spaced paper without DOUBLE spaced paragraphs. So I wanted folks who have brain fog/neuro lymies to go half way down the entire post to look for my version.

It's shorter paragraphs and DOUBLE SPACED; I copied to my wordperfect and it was 29 pages after I edited it.

I changed nothing; just made shorter sentences and more space for our minds that can't comprehend single spaced stuff with no spaces in between. Enjoy.

Margie, I'm going to email Megan myself and give her this thread address so she can read people's comments about her outstanding work, and also have a double=spaced one if she wants for herself or others. [Big Grin]

Bettyg

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caat
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Daystar, are you sure she wanted this posted on lymenet board?

Megan, wow. This is impressive! I think you should get it copywritten if you haven't already. I hope they are letting you take college courses NOW... and independant studies. If not, they really should be. If that's what you want.

Most of this is waaay over my head so I can't give much feedback. I hope someone who has written studies can help you to polish it up and check it. But it is wonderful. It looks like it just needs structural checking from someone experienced in writing studies. Maybe it doesn't need anything.

I was impressed that you mentioned health care as a variable;

"The use of death rates rather than diagnosis rates provided more definitive information, though it introduced the variable of healthcare.
States which have higher rates of diagnosis, in fact, sometimes display lower death rates, because, with experience, doctors in those areas often are more familiar with treating the disease. "

That could *also* possibly come into play with African Americans having a lower rate of MS (and probably lyme), but statistically a higher rate of syphilis... Syphilis acts simular to lyme, and there is definitely bias in diagnostics and secondary testing depending on race and class...

A classic racial diagnostic difference is cancer. I don't know what the statistics are now, but it used to be that African Americans had a lower statistical rate of cancer, but a much higher death rate when cancer was (finally) diagnosed. It is thought to be because of differences in economics and health care. And having been to public health clinics- I'm sure it is... That could be a whole other data review later on if you were interested in writing more reviews... It could be interesting and might actually work to change a few things.

Something else to consider, just for yourself and maybe not in this report. Looks like you have plenty of information for this report! The death rates for lyme may be under reported. Lyme has a tendency to harm the imune system and some of us feel that people generally die of complications and co-infections rather than strictly lyme. When people do die, I think it's not often that the cause of death is listed as lyme.

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daystar1952
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Betty G...That's a good idea to send her the messages.

And yes, she told me it was ok to share it with others. She did have a copywrite mark on the paper somewhere

Margie

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daystar1952
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Caat...I'm not sure what you meant by possible structural changes but when I copied and pasted it to the board, it lost all the structure of the charts and maps. If anyone wants I can send them the unaltered attachment
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caat
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Hi Daystar,

I meant the way the information is presented, not the formatting on the board. I'm not sure, but I think some of the stuff that is included in the main body might be footnote material for instance. Or some other way of organization.

BUT- I really don't know. I have NO idea. Would be good if someone who has written studies could check that out for her. I'm sure someone would be glad to look it over for that, but not sure how many researchers are on this board. Maybe one of the researchers that she contacted might help. Or maybe a new post asking for researchers might bring someone up.

Glad she's got the copywrite there. That's great material and a lot of work [Smile]

I'd love to see the charts, but just for myself. I doubt I'd be able to give any constructive feedback on it.

If you change the (AT) to @ in my email below it would work. If it's OK, I'd love to see the original attachment.

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bettyg
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NEWS FLASH BULLETIN:
Surprise! I heard from Megan herself early this morning!

What a wonderful email it was too. She has read ALL of our comments on daystar's post here.

I sent her a note back asking if I could post what she wrote me or if she might like to do it herself? So until I here different, I won't post it.

But I will tell you that she's so happy for our input as she did this for us LYMIES, MS, and breast cancer patients.

She also explained WHY she picked this subject.

She sent me too her original paper with the TABLES in tact that made much more sense than my double spacing things, etc.

I don't know how to forward this info to others, so please don't ask me for this,

but ask DAYSTAR since she knows how, and since she said she'd be happy to email anyone requesting this ok!

As a afterthought after I went to bed after her note, I thought of this also, and sent another email when I got on here this morning.

I asked her as time permits if she might consider writing a SUMMARY for us lymies in user-friendly English so we could grasp what she was saying with all our neuro lyme brains.

I told her she did not have to do this; that this was just a thought I had. I'll wait her answer on that too.

What a sweet, young lady so caring about her friends her age with unknown illnesses and all of us lymies, MS, and breast cancer patients.

Did I make your day; Megan made mine!

So don't be surprised if you see her respond, or better yet, if she does come on here, I'll suggest she start HER own thread so ALL replies go to her home mail.

Bettyg

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bettyg
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up, no one's seen my news flash...shucky darn! [Wink]
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up;

No excitement from anyone that I heard back from Megan about her outstanding thesis? Betty [confused]

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Ann-OH
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up and thank you to Betty!

--------------------
www.ldbullseye.com

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troutscout
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I'd like a full copy.

[email protected]


This is fantastic.


Trout [Wink]

--------------------
Now is the time in your life to find the "tiger" within.
Let the claws be bared,
and Lyme BEWARE!!!
www.iowalymedisease.com
[/URL]  -

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bettyg
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up...this is too good of info to be buried..

I'll check with Treepatrol if he'll post this link there for folks to read for a long time!

Bettyg

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trails
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I'd really like a copy like you speak of! Wtih table and charts. Can we post it up here somehow?
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humanbeing
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I would love to see all the graphs and charts too.

Megan is awsome and I wish my 15 year old could be less interested in her hair straightener and more interested in topics that can make a difference.

A++++

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We are spiritual beings on a human journey...

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daystar1952
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Can you guys send me your emails and I will send you the whole attachment?

Margie

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lightfoot
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Wow!

Megan! Daystar! Betty and all!!!

I would like the full attachment.

That way I can see it in totality after primting it.....I can sit with my Lyme challenged brain and study it!!

My e-mail is in my signature.

Margie, thanks in advance!! You have been a wonderful resource for Megan (and all of us on so many occasions)!!

Healing smiles....lightfoot [Smile] [Smile] [Smile]

--------------------
Healing Smiles.....lightfoot [Smile] [Smile] [Smile]

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Tom Grier
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Yes I would also like the attachment of the full article.

What a serious piece of research for a young student. It is very encouraging. Tom Grier

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krazykt1
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Thank you Megan...Outstanding !!!
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Neil M Martin
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Daystar52 : thanks for this post. Please email me the attachment. [email protected]

Is there an http?

Megan : Great Work. Well done!

You may have explained a cause of T cell lymphoma (TCL) and Guillain Barre Syndroms (GBS). I have been diagnosed with both. GBS attacks myelin sheaths of peripheral nerves. I think MS attacks the CNS.

Cutaneous TCL turns skin raw or tumorous.

In either case, stealth pathogens are not usually suspected.

Steriods and UV light treatments made my symptoms worse. Antibiotics and pH-neutralizing helped - a negative environment for "spirochetal activity."

Your work is a tribute to Dr. Mattman. Has she seen it?

Neil

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Neil

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bettyg
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Daystar/all,

I started a new thread with the email she sent me & another one which includes a BRIEF SUMMARY of her work.

http://flash.lymenet.org/ubb/ultimatebb.php?ubb=get_topic;f=1;t=041853

She has been reading here & MAY come to post if she can figure it out. LOL [Smile]

Bettyg

[ 06. March 2006, 01:29 AM: Message edited by: bettyg ]

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mbresearch
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Hello, this is Megan. I am truly thrilled to see that so many people are reading and commenting on the paper. It is really wonderful to hear from Lyme members themselves.

Several people mentioned taking into account outside variables such as age, race, and available healthcare. I briefly discussed some of those variables, though now that I have heard your comments I am planning on during a more intensive multiple regression to better determine what role each of those factors plays.

In the process of creating this paper, I emailed all that state epidemiologists in the nation. Although Lyme is a reportable illness, I found that obtaining Lyme incidence data was sadly very difficult. The data as it was reported does not include demographic information. Using database techniques, I have been able to associate other variables such as latitude, longitude, and elevation.

Thank you again for your support. I am hoping this database will be of value to Lyme researchers everywhere.

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Andie333
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Megan, you're the best!

Don't stop doing what you're doing; the world needs you!

Andie

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bettyg
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breaking this up and copying also to other post so those folks can read Megan will be doing more on her big project! Way to go Megan! We love you!

quote:
Originally posted by mbresearch:
Hello, this is Megan.
I am truly thrilled to see that so many people are reading and commenting on the paper. It is really wonderful to hear from Lyme members themselves.

Several people mentioned taking into account outside variables such as age, race, and available healthcare.

I briefly discussed some of those variables, though now that I have heard your comments I am planning on during a more intensive multiple regression to better determine what role each of those factors plays.

In the process of creating this paper, I emailed all that state epidemiologists in the nation.

Although Lyme is a reportable illness, I found that obtaining Lyme incidence data was sadly very difficult. The data as it was reported does not include demographic information.

Using database techniques, I have been able to associate other variables such as latitude, longitude, and elevation.

Thank you again for your support. I am hoping this database will be of value to Lyme researchers everywhere.

Megan, THANKS so much on behalf of us for coming on here tonight for the 1st time and posting your comments about your college level thesis.

Happy to read you plan on expanding it as well.

your online friend, Bettyg

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adding another link with LYME/MS deaths in USA maps....

http://flash.lymenet.org/ubb/ultimatebb.php?ubb=get_topic;f=1;t=042406

Betty

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