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


© Megan M. Blewett 2006

Megan.Blewett@att.net


Abstract

 

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 disorderās 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 studyās 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 ćmost common primary neurological disorder of young adultsä (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 ćlinked 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; Lymeinfo.net, 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 ćAn 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.ä NINDS (2006b) also recognizes the neurological complications of Lyme, which usually occur in the second stage, and include ćnumbness, 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.ä

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 Gehrigās 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, ćMortality ö Underlying Cause-of-Death ö 1998ä (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 ćMortalityä 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, ćthe 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.ä (CDC, 2005d, Introduction section). ćThe 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.ä (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) ö 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) ö includes Erythema Chronicum Migrans, Babesiosis (088.82) ö 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 ö 174.9

 

Neoplasms (II: 140-239), Malignant Neoplasm of the Female Breast (174) öIncludes 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 Gehrigās 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) ö 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



Delawareās 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 stateās 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 unitās 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 ćsuper-countyä. 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 stateās 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 ćOther Specified Arthropod Borne Diseasesä 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 stateās 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 stateās 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 stateās 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 variableās 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 ö2 and +2, then the distribution is symmetrical (normal). If the ratio of the kurtosis value to its standard error is between ö2 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 Kendallās tau-b or Spearmanās 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 Pearsonās, Kendallās, or Spearmanās 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

Kendallās tau_b: 0.213*

Sig. (2-tailed): 0.028

Spearmanās 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

Kendallās tau_b: 0.222*

Sig. (2-tailed): 0.022

Spearmanās 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

Kendallās tau_b: 0.269**

Sig. (2-tailed): 0.005

Spearmanās rho: 0.404**

Sig. (2-tailed): 0.003

Kendallās tau_b: 0.286**

Sig. (2-tailed): 0.003

Spearmanās rho: 0.401**

Sig. (2-tailed): 0.004

Centroid Latitude

51

Kendallās tau_b: 0.522**

Sig. (2-tailed): 0.000

Spearmanās rho: 0.669**

Sig. (2-tailed): 0.000

Kendallās tau_b: 0.529**

Sig. (2-tailed): 0.000

Spearmanās 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

Kendallās tau_b: 0.119**

Sig. (2-tailed): 0.000

Spearmanās rho: 0.174**

Sig. (2-tailed): 0.000

Kendallās tau_b: 0.068*

Sig. (2-tailed): 0.023

Spearmanās rho: 0.101**

Sig. (2-tailed): 0.024

OSABD Death Incidence per 100,000 Deaths (1998)

504

Kendallās tau_b: 0.064*

Sig. (2-tailed): 0.035

Spearmanās rho: 0.094*

Sig. (2-tailed): .0360

Kendallās tau_b: 0.079**

Sig. (2-tailed): 0.009

Spearmanās rho: 0.114**

Sig. (2-tailed): 0.010

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

504

Kendallās tau_b: 0.144**

Sig. (2-tailed): 0.000

Spearmanās 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.

Kendallās tau_b: 0.099**

Sig. (2-tailed): 0.001

Spearmanās rho: 0.146**

Sig. (2-tailed): 0.001

Motor Neuron Death Incidence per 100,000 Live (1990)

504

Kendallās tau_b: 0.134**

Sig. (2-tailed): 0.000

Spearmanās rho: 0.183**

Sig. (2-tailed): 0.000

Kendallās tau_b: 0.076*

Sig. (2-tailed): 0.011

Spearmanās rho: 0.106*

Sig. (2-tailed): 0.017

Motor Neuron Death Incidence per 100,000 Deaths (1998)

504

Kendallās tau_b: 0.091**

Sig. (2-tailed): 0.002

Spearmanās rho: 0.125**

Sig. (2-tailed): 0.005

Kendallās tau_b: 0.114**

Sig. (2-tailed): 0.000

Spearmanās rho: 0.155**

Sig. (2-tailed): 0.000

External Cause Death Incidence per 100,000 Live (1990)

504

No statistically significant correlation.

Kendallās tau_b: -0.073*

Sig. (2-tailed): 0.016

Spearmanās rho: -0.108*

Sig. (2-tailed): 0.015

External Cause Death Incidence per 100,000 Deaths (1998)

504

Kendallās tau_b: -0.079**

Sig. (2-tailed): 0.009

Spearmanās rho: -0.117**

Sig. (2-tailed): 0.008

No statistically significant correlation.

Centroid Latitude

504

Kendallās tau_b: 0.173**

Sig. (2-tailed): 0.000

Spearmanās rho: 0.249**

Sig. (2-tailed): 0.000

Kendallās tau_b: 0.203**

Sig. (2-tailed): 0.000

Spearmanās 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.

Kendallās tau_b: 0.152*

Sig. (2-tailed): 0.014

Spearmanās rho: 0.221*

Sig. (2-tailed): 0.014

External Cause Death Incidence per 100,000 Live (1990)

123

No statistically significant correlation.

Kendallās tau_b: -0.152*

Sig. (2-tailed): 0.013

Spearmanās rho: -0.221*

Sig. (2-tailed): 0.014

Centroid Latitude

123

Kendallās tau_b: 0.136*

Sig. (2-tailed): 0.027

Spearmanās rho: 0.199*

Sig. (2-tailed): 0.027

Kendallās tau_b: 0.134*

Sig. (2-tailed): 0.029

Spearmanās rho: 0.196*

Sig. (2-tailed): 0.029

Centroid Longitude

123

Kendallās tau_b: 0.129*

Sig. (2-tailed): 0.035

Spearmanās rho: 0.192*

Sig. (2-tailed): 0.033

Kendallās tau_b: 0.149*

Sig. (2-tailed): 0.016

Spearmanās 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 macrophagesā 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 bodyās 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 bodyās 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 bodyās 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 bacteriaās 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 bodyās 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 MRIās 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.

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