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The Spatial Scan Statistic. Null Hypothesis. The risk of disease is the same in all parts of the map. . One-Dimensional Scan Statistic. The Spatial Scan Statistic. Create a regular or irregular grid of centroids covering the whole study region.

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Presentation Transcript
null hypothesis
Null Hypothesis

The risk of disease is the same

in all parts of the map.

the spatial scan statistic4
The Spatial Scan Statistic
  • Create a regular or irregular grid of centroids covering the whole study region.
  • Create an infinite number of circles around each centroid, with the radius anywhere from zero up to a maximum so that at most 50 percent of the population is included.
slide6

For each circle:

  • Obtain actual and expected number of cases inside and outside the circle.
  • Calculate Likelihood Function.
  • Compare Circles:
  • Pick circle with highest likelihood function as Most Likely Cluster.
  • Inference:
  • Generate random replicas of the data set under the null-hypothesis of no clusters (Monte Carlo sampling).
  • Compare most likely clusters in real and random data sets (Likelihood ratio test).
spatial scan statistic properties
Spatial Scan Statistic: Properties
  • Adjusts for inhomogeneous population density.
  • Simultaneously tests for clusters of any size and any location, by using circular windows with continuously variable radius.
  • Accounts for multiple testing.
  • Possibility to include confounding variables, such as age, sex or socio-economic variables.
  • Aggregated or non-aggregated data (states, counties, census tracts, block groups, households, individuals).
slide8

Breast Cancer Incidence, Relative Risks

Age-Adjusted, Indirect Standardization

four most likely clusters
Four Most Likely Clusters

p=0.99

p=0.11

p=0.37

p=0.88

four most likely clusters11
Four Most Likely Clusters

Cluster Obs Exp RR p=

East 1853 1722 1.08 0.11

Central 986 899 1.10 0.37

Southwest 51 36 1.43 0.89

Northwest 199 172 1.16 0.99

geographical aggregation
Geographical Aggregation
  • In traditional mapping of rates or relative risks for disjoint geographical areas, there is a trade-off between the stability of the estimates and the geographical resolution.
  • With tests for spatial randomness, less geographical data aggregation is always better:
  • Ability to detect clusters not conforming to political boundaries.
  • More accurate data / less loss of information.
slide14

Eight Most Likely Clusters

for Breast Cancer Incidence

(approximate locations)

iowa breast cancer incidence
Iowa Breast Cancer Incidence

Census Tract Aggregation

Cluster Obs Exp RR LLR p=

1 341 240 1.4 19.4 0.001

2 28 11 2.6 9.8 0.03

3 1843 1708 1.1 6.7 0.39

4 29 15 2.0 5.3 0.80

5 21 10 2.1 4.4 0.98

6 30 17 1.8 4.4 0.98

7 208 171 1.2 3.8 0.99

8 41 26 1.6 3.8 0.99

iowa breast cancer staging
Iowa Breast Cancer Staging

Census Tract Aggregation

Late Stage Cases: 758

Total Cases: 7415

slide17

Six Most Likely Clusters

of Late Stage Breast Cancer

B

C

A

F

E

D

late stage breast cancer
Late Stage Breast Cancer

Census Tract Aggregation

Cluster Obs Exp RR LLR p =

A 15 4.5 3.3 9.2 0.049

B 13 4.7 2.8 5.9 0.62

C 6 1.3 4.5 5.5 0.75

D 44 27.1 1.6 5.3 0.81

E 9 3.1 2.9 4.5 0.97

F 4 0.9 3.5 4.3 0.99

summary breast cancer in iowa
Summary: Breast Cancer in Iowa
  • A cluster of high breast cancer incidence was found west of Des Moines.
  • The geographical distribution of late stage breast cancer is rather even, with only one marginally significant cluster
summary spatial scan statistic
Summary: Spatial Scan Statistic
  • Cluster detection irrespectively of political boundaries, and without assumptions about cluster size or location.
  • Adjusts for multiple testing.
  • It is only possible to pinpoint the general location of a cluster. The borders are approximate.
  • It is a surveillance tool. The cause of a cluster must be investigated through other means.
two complimentary maps
Two Complimentary Maps

A map with smoothed disease rates provides a rate estimate for all parts of the map, but it does not tell us whether the pattern is random or not.

A map based on the spatial scan statistic tells us if and where there are areas with a significantly higher disease rate, but it does not provide a rate estimate for all parts of the map.

breast cancer mortality northeastern united states
Breast Cancer MortalityNortheastern United States

States: Maine, N.H., Vermont, Mass., R.I., Connecticut, N.Y., N.J., Pennsylvania, Delaware, Maryland, D.C.

Years: 1988-1992

Deaths: 58,943

Population: 29,535,210

Geographical Aggregation: 245 counties

Joint work with: E Feuer, B Miller, L Freedman, NCI

slide25

Breast cancer mortality

Most likely cluster

p=0.001

most likely clusters
Most Likely Clusters

Location Obs Exp RR LLR p

NY/Philadelphia 24,044 23,040 1.074 35.7 0.001

Buffalo 1,416 1,280 1.109 7.1 0.12

Washington DC 712 618 1.154 6.9 0.15

Boston 5,966 5,726 1.047 5.5 0.40

Eastern Maine 267 229 1.166 3.0 0.99

references
References

General Theory

Kulldorff M. A Spatial Scan Statistic, Communications in Statistics, Theory and Methods, 26:1481-1496, 1997.

Application

Kulldorff M. Feuer E, Miller B, Freedman L. Breast Cancer in Northeast United States: A Geographic Analysis. American Journal of Epidemiology, 146:161-170, 1997.