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. 2022 Aug 5;9:30–32. doi: 10.1016/j.jdin.2022.07.006

Table I.

SaTScan cluster analysis results

Cluster number Location Radius (km) Cases per 100,000 Relative risk P value
Not controlling for race
 1 Cape May County, NJ 410.40 50.6 1.68 <.0001
 2 West Princess Anne, MD 181.38 69.4 2.22 <.0001
 3 Waterford Township, PA 173.50 54.4 1.76 <.0001
 4 Huron Township. OH 255.84 47.3 1.54 <.0001
 5 Dothan, AL 450.35 46.5 1.52 <.0001
 6 Tuscaloosa County, AL 306.78 57.1 1.82 <.0001
 7 Bloomingdale, GA 277.49 51.9 1.66 <.0001
 8 Queensbury, NY 299.32 47.7 1.53 <.0001
 9 Jackson, MI 124.46 57.7 1.82 <.0001
 10 Forsyth County, NC 167.86 42.6 1.34 <.0001
 11 Fort Thomas, KY 168.87 37.7 1.19 <.0001
 12 Little Rock, AR 350.61 39.8 1.24 .0005
 13 Lexington, KY 110.94 38.7 1.21 .0006
 14 Sumter County, FL 0 13 3.51 .142
 15 Highlands County, FL 75.89 109 1.40 .439
 16 Shreveport, LA 246.98 237 1.22 .772
 17 Scipio Township, IN 56.25 98 1.32 .976
Controlling for race
 1 Natrona County, WY 0 3 1.00 1.00

Clusters were identified using the number of cases per core-based statistical area (CBSA) with the total CBSA population and using a latitude and longitude coordinate system. The cluster analysis was run using a discrete Poisson probability model, with 10% of the population at risk set as the maximum spatial cluster size.