1. Spatial Clustering of Scleroderma in Three Michigan Counties
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Spatial Clustering of Scleroderma in Three Michigan Counties
“The Toledo Twins”
Sharon HensleyAlford
Sarah Ann Cleveland
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2. Background The disease that “turns people to stone” Classifications
Chronic, connective tissue disease
Unknown cause
Collagen accumulation in some organs
Classifications
Localized
Systemic

3. Who has scleroderma
Approximately 150,000 people in the United States
4 times more women than men

4. Symptoms May Include
Raynaud’s Phenomenon
Swelling of hands and feet
Pain and stiffness of joints
Thickening of the skin
Kidney, heart, and lung involvement
Oral, facial, and dental problems

5. Diagnosis Difficult Involvement of several specialists
May take months to years

6. Rates in Study Area Prevalence 242 cases per 1million adults
Incidence
19 new cases per 1 million adults per year
Reference: Prevalence, incidence and survival rates of systemic sclerosis in the Detroit metropolitan area. Mayes et al.

7. Age Distribution of Cases

8. Sex Distribution 134 Females 37 Males
3.62 times more females than males

9. Etiologies UNKNOWN Possibilities include: silica dust
vinyl chloride monomer
pet ownership
some solvents
appetite suppressants

10. Significance To Explore... Environmental associations
Spatial pattern  Disease process

11. Materials Disease Data Population Data
Incidence data for (N=171)
Three counties:
Macomb
Oakland
Wayne
Population Data
1990 Census data
Block and Tract divisions

12. Methods Gathering population data Geocoding cases
Finding census data
Extract pertinent information
Geocoding cases
ArcView Batch Match
Digitizing unmatched cases

13.  Working with Unmatched Cases 
Verifying address: US Post:
Using web map programs
Mapquest:
gives county lines
Vicinity:
Digitizing

14. Matching Rate 161 Matched/166 Total 96% Matching Rate 171 Original
Batch-Matched Addresses
n=148
Non-matched
n=18
Digitized
n=13
Unable to Digitize
n=5
5 Unmatchable Addresses
n=166
161 Matched/166 Total
96% Matching Rate

18. Statistical Analysis
Null Hypothesis: No spatial clustering
Alter. Hypothesis: Spatial clustering
Test statistic: Ipop, Moran’s I
Statistical Program: STAT!

19. Macomb/Tracts STAT Output 4/13/1999 Assumption R
Results from Ipop test
Number of runs :
Ipop calculations
Areas (m) :
cases (n) :
Population (x) :
Ipop :
Ipop' :
E[I] :
% within :
% among :
Assumption R
Variance :
z-score :
Significance : (2-tailed)
Approximation
z-score :
Significance : (2-tailed)
Simulation
Significance : (2-tailed)

20. Macomb/Block STAT Output 4/14/1999 Results from Ipop test
Number of runs :
Ipop calculations
Areas (m) :
cases (n) :
Population (x) :
Ipop :
Ipop' :
E[I] :
% within :
% among :
Assumption R
Variance :
z-score :
Significance : (2-tailed)
Approximation
z-score :
Significance : (2-tailed)
Simulation
Significance : (2-tailed)

21. Oakland/Tracts STAT Output 4/13/1999 Results from Ipop test
Number of runs :
Ipop calculations
Population (x) :
Ipop :
Ipop' :
E[I] :
% within :
% among :
Assumption R
Variance :
z-score :
Significance : (2-tailed)
Approximation
z-score :
Significance : (2-tailed)
Simulation
Significance : (2-tailed)

22. Oakland/Blocks

23. Wayne/Tracts STAT Output 4/13/1999 Results from Ipop test
Number of runs :
Ipop calculations
Areas (m) :
cases (n) :
Population (x) :
Ipop :
Ipop' :
E[I] :
% within :
% among :
Assumption R
Variance :
z-score :
Significance : (2-tailed)
Approximation
z-score :
Significance : (2-tailed)
Simulation
Significance : (2-tailed)

24. Wayne/Blocks

25. Discussion Limitations of Analysis Future Analysis
Position uncertainty
Residential history
Reliability of census data
Future Analysis
Stratification by age, sex, race
3 county combination analysis
Space/Time Analysis

26. First Honors: Andy Long
Thank You
First Honors: Andy Long
Mark Wilson
Dr. Mayes
Geoff Jacquez